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+lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000546325.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000451714.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000555009.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000551780.zst filter=lfs diff=lfs merge=lfs -textlambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000369503.zst filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e33961a86f75becae0d5d25e8d0e9957de083e9a --- /dev/null +++ b/README.md @@ -0,0 +1,9 @@ +--- +license: mit +language: + - en +models: + - stabilityai/stable-diffusion-3.5-large +tags: + - large-model-feature-coding +--- diff --git a/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log b/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..afa5ecff3ad19ef122f8a431399f5288ddbb6570 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log @@ -0,0 +1,21862 @@ +Experiment: dtufc_elic-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: sd35 + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 286 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 392B, BPFP=4.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,252B, BPFP=0.5752 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,864B, BPFP=0.3136 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,412B, BPFP=0.5939 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,804B, BPFP=0.1038 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,804B, BPFP=0.1038 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,020B, BPFP=0.0616 +⌛️ [2/4] FRONTEND: Frontend time: 3.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.649s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.98285135 + text_encoder-item0.clip_prompt_embeds 0.00025464 24.13778409 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.89578266 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.28113096 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.01094694 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00635250 5.63317633 + vae.encoder_f1 0.00635834 5.63302755 + vae.decoder 0.00019940 0.08836941 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 4.48315194 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 82248 +BPFP 0.2910 bits/point +EBPFP 0.5820 equivalent bits/point +MSE 4.483152 +---------------------- -------------------------------------------------------- +Time: 4.799s Load: 0.007s, Pack+Encode: 3.142s, Decode+Unpack: 1.649s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4832 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,204B, BPFP=0.5687 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 596B, BPFP=3.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,620B, BPFP=0.3750 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,712B, BPFP=0.5761 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,076B, BPFP=0.0927 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,076B, BPFP=0.0927 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,296B, BPFP=0.0701 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.97053838 + text_encoder-item0.clip_prompt_embeds 0.00022609 24.15992374 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.93540077 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.30117869 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00944030 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.01130640 5.49005890 + vae.encoder_f1 0.01130902 5.49318361 + vae.decoder 0.00020860 0.06828294 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 4.41646989 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81064 +BPFP 0.2868 bits/point +EBPFP 0.5737 equivalent bits/point +MSE 4.416470 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.009s, Pack+Encode: 2.142s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4165 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,548B, BPFP=0.4800 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,488B, BPFP=0.4455 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,832B, BPFP=0.5538 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,432B, BPFP=0.0676 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,432B, BPFP=0.0676 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,916B, BPFP=0.0585 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 1.00154018 + text_encoder-item0.clip_prompt_embeds 0.00022402 24.16632212 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 1.15194073 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.28200844 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.01141422 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 1.19630027 7.48225260 + vae.encoder_f1 1.19630098 7.48225975 + vae.decoder 0.00023596 0.05342209 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 5.33768006 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 76712 +BPFP 0.2714 bits/point +EBPFP 0.5429 equivalent bits/point +MSE 5.337680 +---------------------- -------------------------------------------------------- +Time: 3.735s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.3377 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,992B, BPFP=0.5400 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,336B, BPFP=0.4331 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,168B, BPFP=0.5623 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,780B, BPFP=0.0882 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,776B, BPFP=0.0881 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,220B, BPFP=0.0677 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.95913498 + text_encoder-item0.clip_prompt_embeds 0.00030342 24.14815848 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.91258974 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.29200099 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.01045617 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00586287 3.61977649 + vae.encoder_f1 0.00587438 3.62048197 + vae.decoder 0.00017677 0.11855990 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 3.55377791 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80352 +BPFP 0.2843 bits/point +EBPFP 0.5686 equivalent bits/point +MSE 3.553778 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5538 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,120B, BPFP=0.4221 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 600B, BPFP=3.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,972B, BPFP=0.4036 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,784B, BPFP=0.5272 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,912B, BPFP=0.0750 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,904B, BPFP=0.0748 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,936B, BPFP=0.0591 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.92672022 + text_encoder-item0.clip_prompt_embeds 0.00024120 24.18158778 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 1.01336870 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.28575180 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00857447 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00779453 5.37561560 + vae.encoder_f1 0.00779802 5.37735939 + vae.decoder 0.00023829 0.06519622 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 4.36251926 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 75728 +BPFP 0.2679 bits/point +EBPFP 0.5359 equivalent bits/point +MSE 4.362519 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.007s, Pack+Encode: 2.147s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.3625 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 376B, BPFP=3.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,472B, BPFP=0.3630 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,596B, BPFP=0.5478 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,532B, BPFP=0.1149 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,524B, BPFP=0.1148 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,200B, BPFP=0.0671 +⌛️ [2/4] FRONTEND: Frontend time: 2.133s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.97398551 + text_encoder-item0.clip_prompt_embeds 0.00025651 24.14724745 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.97922897 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.29912858 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.01175591 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00655775 6.36966753 + vae.encoder_f1 0.00656268 6.35528946 + vae.decoder 0.00020283 0.07316829 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 4.82084021 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 82428 +BPFP 0.2917 bits/point +EBPFP 0.5833 equivalent bits/point +MSE 4.820840 +---------------------- -------------------------------------------------------- +Time: 3.731s Load: 0.008s, Pack+Encode: 2.133s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8208 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 376B, BPFP=3.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,676B, BPFP=0.4973 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,848B, BPFP=0.3935 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,708B, BPFP=0.5253 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,024B, BPFP=0.1072 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,036B, BPFP=0.1074 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,056B, BPFP=0.0627 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.99713278 + text_encoder-item0.clip_prompt_embeds 0.00022242 24.16396527 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.96840534 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.25489526 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.01025587 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00593415 4.29291248 + vae.encoder_f1 0.00594307 4.28838587 + vae.decoder 0.00018992 0.09623761 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 3.86096808 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80420 +BPFP 0.2845 bits/point +EBPFP 0.5691 equivalent bits/point +MSE 3.860968 +---------------------- -------------------------------------------------------- +Time: 3.727s Load: 0.009s, Pack+Encode: 2.130s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8610 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,092B, BPFP=0.5536 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,328B, BPFP=0.2701 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,320B, BPFP=0.5662 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,976B, BPFP=0.1064 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,972B, BPFP=0.1064 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,556B, BPFP=0.0780 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.94957813 + text_encoder-item0.clip_prompt_embeds 0.00022110 24.14593479 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.94416571 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.31809568 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00961290 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00641770 5.19424725 + vae.encoder_f1 0.00642053 5.19433546 + vae.decoder 0.00017498 0.06966200 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 4.27913113 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81328 +BPFP 0.2878 bits/point +EBPFP 0.5755 equivalent bits/point +MSE 4.279131 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2791 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 2,812B, BPFP=0.3804 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,304B, BPFP=0.3494 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,084B, BPFP=0.5602 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,816B, BPFP=0.0735 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,820B, BPFP=0.0735 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,892B, BPFP=0.0577 +⌛️ [2/4] FRONTEND: Frontend time: 2.125s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.95419860 + text_encoder-item0.clip_prompt_embeds 0.00021654 24.15813760 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 1.02003555 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.28719415 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00998060 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00577698 3.55745482 + vae.encoder_f1 0.00578348 3.55677867 + vae.decoder 0.00017559 0.09001155 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 3.52128905 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 75800 +BPFP 0.2682 bits/point +EBPFP 0.5364 equivalent bits/point +MSE 3.521289 +---------------------- -------------------------------------------------------- +Time: 3.725s Load: 0.009s, Pack+Encode: 2.125s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5213 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,696B, BPFP=0.5000 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,764B, BPFP=0.3867 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,824B, BPFP=0.5789 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,704B, BPFP=0.1023 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,704B, BPFP=0.1023 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,772B, BPFP=0.0541 +⌛️ [2/4] FRONTEND: Frontend time: 2.133s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.99890939 + text_encoder-item0.clip_prompt_embeds 0.00022160 24.16883751 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.85725489 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.28117813 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00968985 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00668450 4.13008165 + vae.encoder_f1 0.00668875 4.13034678 + vae.decoder 0.00023059 0.07225477 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 3.78491471 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81544 +BPFP 0.2885 bits/point +EBPFP 0.5770 equivalent bits/point +MSE 3.784915 +---------------------- -------------------------------------------------------- +Time: 3.736s Load: 0.009s, Pack+Encode: 2.133s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7849 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 376B, BPFP=3.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,644B, BPFP=0.4930 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 596B, BPFP=3.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,336B, BPFP=0.3519 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,252B, BPFP=0.5898 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,748B, BPFP=0.1182 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,748B, BPFP=0.1182 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,504B, BPFP=0.0459 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.95598094 + text_encoder-item0.clip_prompt_embeds 0.00023190 24.17785275 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.79597254 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.29217989 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.01025546 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.04018118 6.26009989 + vae.encoder_f1 0.04018488 6.26013756 + vae.decoder 0.00016201 0.04441109 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 4.77021326 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 83320 +BPFP 0.2948 bits/point +EBPFP 0.5896 equivalent bits/point +MSE 4.770213 +---------------------- -------------------------------------------------------- +Time: 3.732s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7702 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,656B, BPFP=0.4946 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,948B, BPFP=0.4016 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,852B, BPFP=0.5543 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,420B, BPFP=0.1132 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,416B, BPFP=0.1132 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,144B, BPFP=0.0959 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.98965502 + text_encoder-item0.clip_prompt_embeds 0.00023140 24.16728812 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.91015978 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.27612243 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.01009127 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.04874706 7.68919706 + vae.encoder_f1 0.04875064 7.68055105 + vae.decoder 0.00019641 0.06903116 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 5.43290075 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 83508 +BPFP 0.2955 bits/point +EBPFP 0.5909 equivalent bits/point +MSE 5.432901 +---------------------- -------------------------------------------------------- +Time: 3.739s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.4329 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,760B, BPFP=0.5087 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 604B, BPFP=3.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,056B, BPFP=0.4104 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,408B, BPFP=0.5430 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,344B, BPFP=0.1273 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,336B, BPFP=0.1272 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,332B, BPFP=0.0406 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.94525846 + text_encoder-item0.clip_prompt_embeds 0.00030893 24.13466839 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.93691378 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.27483487 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00895961 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.01360236 7.97659874 + vae.encoder_f1 0.01360807 7.94816160 + vae.decoder 0.00023006 0.04976268 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 5.55829812 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 83344 +BPFP 0.2949 bits/point +EBPFP 0.5898 equivalent bits/point +MSE 5.558298 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.5583 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 392B, BPFP=4.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,304B, BPFP=0.5823 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 568B, BPFP=3.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,960B, BPFP=0.3214 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,152B, BPFP=0.5873 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,412B, BPFP=0.0521 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,412B, BPFP=0.0521 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 816B, BPFP=0.0249 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.96439727 + text_encoder-item0.clip_prompt_embeds 0.00024198 24.18045057 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.95836315 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.30282508 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00968835 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 1.67190456 10.07607651 + vae.encoder_f1 1.67190480 10.07609940 + vae.decoder 0.00017417 0.02470559 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 6.53820109 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 74132 +BPFP 0.2623 bits/point +EBPFP 0.5246 equivalent bits/point +MSE 6.538201 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.007s, Pack+Encode: 2.144s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.5382 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,464B, BPFP=0.4686 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 600B, BPFP=3.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,688B, BPFP=0.3805 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,116B, BPFP=0.5863 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,044B, BPFP=0.1075 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,044B, BPFP=0.1075 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,548B, BPFP=0.0778 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.91452130 + text_encoder-item0.clip_prompt_embeds 0.00025129 24.13571471 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.93395958 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.27811345 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.01013602 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00621760 5.59225368 + vae.encoder_f1 0.00622505 5.58920479 + vae.decoder 0.00025114 0.08633561 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 4.46296465 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 83004 +BPFP 0.2937 bits/point +EBPFP 0.5874 equivalent bits/point +MSE 4.462965 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4630 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 2,872B, BPFP=0.3885 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,412B, BPFP=0.3581 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,204B, BPFP=0.5378 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,468B, BPFP=0.1292 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,468B, BPFP=0.1292 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,464B, BPFP=0.0752 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.98168969 + text_encoder-item0.clip_prompt_embeds 0.00020838 24.14105832 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.95412331 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.29061814 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.01149948 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00675961 7.46221828 + vae.encoder_f1 0.00676652 7.46320009 + vae.decoder 0.00021373 0.09170915 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 5.33266157 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 82956 +BPFP 0.2935 bits/point +EBPFP 0.5870 equivalent bits/point +MSE 5.332662 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.009s, Pack+Encode: 2.141s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.3327 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 2,840B, BPFP=0.3842 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 596B, BPFP=3.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,836B, BPFP=0.3114 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,824B, BPFP=0.5789 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,284B, BPFP=0.0501 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,284B, BPFP=0.0501 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,456B, BPFP=0.0750 +⌛️ [2/4] FRONTEND: Frontend time: 2.124s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.94553932 + text_encoder-item0.clip_prompt_embeds 0.00021387 24.14571496 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.90956221 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.30240479 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00943748 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00596338 2.12444806 + vae.encoder_f1 0.00596322 2.12444282 + vae.decoder 0.00018207 0.14637701 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 2.86359380 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 73616 +BPFP 0.2605 bits/point +EBPFP 0.5209 equivalent bits/point +MSE 2.863594 +---------------------- -------------------------------------------------------- +Time: 3.723s Load: 0.008s, Pack+Encode: 2.124s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8636 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,176B, BPFP=0.4297 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 600B, BPFP=3.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,152B, BPFP=0.3370 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,252B, BPFP=0.5137 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,896B, BPFP=0.0442 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,896B, BPFP=0.0442 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,640B, BPFP=0.0500 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.90837034 + text_encoder-item0.clip_prompt_embeds 0.00022138 24.15707648 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.92459707 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.30336490 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00951084 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00552804 1.53075325 + vae.encoder_f1 0.00552758 1.53074861 + vae.decoder 0.00018040 0.10295933 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 2.58356841 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 70108 +BPFP 0.2481 bits/point +EBPFP 0.4961 equivalent bits/point +MSE 2.583568 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5836 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,092B, BPFP=0.5536 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,548B, BPFP=0.3692 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,444B, BPFP=0.5439 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,376B, BPFP=0.0668 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,380B, BPFP=0.0668 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,560B, BPFP=0.0476 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.98175756 + text_encoder-item0.clip_prompt_embeds 0.00024507 24.17634985 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.97799969 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.28350961 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.01141413 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00721525 3.21476436 + vae.encoder_f1 0.00721777 3.21655512 + vae.decoder 0.00018707 0.06028912 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 3.35998735 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 75480 +BPFP 0.2671 bits/point +EBPFP 0.5341 equivalent bits/point +MSE 3.359987 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3600 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,368B, BPFP=0.4556 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,544B, BPFP=0.3688 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,260B, BPFP=0.5393 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,284B, BPFP=0.1111 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,284B, BPFP=0.1111 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,784B, BPFP=0.0850 +⌛️ [2/4] FRONTEND: Frontend time: 2.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.95827532 + text_encoder-item0.clip_prompt_embeds 0.00046272 24.17621669 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 1.01535149 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.26486900 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.01174744 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.01999603 7.05560398 + vae.encoder_f1 0.01999529 7.05458021 + vae.decoder 0.00024882 0.08242677 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 5.14240396 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81604 +BPFP 0.2887 bits/point +EBPFP 0.5775 equivalent bits/point +MSE 5.142404 +---------------------- -------------------------------------------------------- +Time: 3.732s Load: 0.008s, Pack+Encode: 2.132s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.1424 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,392B, BPFP=0.4589 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,592B, BPFP=0.3727 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,936B, BPFP=0.5310 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,692B, BPFP=0.1174 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,688B, BPFP=0.1173 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,708B, BPFP=0.0521 +⌛️ [2/4] FRONTEND: Frontend time: 2.131s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.95718765 + text_encoder-item0.clip_prompt_embeds 0.00020334 24.13443165 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.88893328 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.28126173 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.01117417 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.01341345 6.90047216 + vae.encoder_f1 0.01341645 6.90155268 + vae.decoder 0.00018350 0.04894200 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 5.06653422 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81080 +BPFP 0.2869 bits/point +EBPFP 0.5738 equivalent bits/point +MSE 5.066534 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.131s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.0665 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,400B, BPFP=0.4600 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 592B, BPFP=3.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,696B, BPFP=0.3812 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,344B, BPFP=0.5668 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,068B, BPFP=0.1078 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,068B, BPFP=0.1078 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,416B, BPFP=0.1042 +⌛️ [2/4] FRONTEND: Frontend time: 2.127s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.93914278 + text_encoder-item0.clip_prompt_embeds 0.00022316 24.16632212 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.95646887 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.27522250 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00972775 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00606298 4.22687149 + vae.encoder_f1 0.00607096 4.21651030 + vae.decoder 0.00023408 0.11043759 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 3.83148142 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 83084 +BPFP 0.2940 bits/point +EBPFP 0.5879 equivalent bits/point +MSE 3.831481 +---------------------- -------------------------------------------------------- +Time: 3.728s Load: 0.008s, Pack+Encode: 2.127s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8315 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,512B, BPFP=0.4751 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 596B, BPFP=3.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,328B, BPFP=0.2701 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,400B, BPFP=0.5428 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,044B, BPFP=0.0922 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,044B, BPFP=0.0922 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,044B, BPFP=0.0929 +⌛️ [2/4] FRONTEND: Frontend time: 2.127s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.93302290 + text_encoder-item0.clip_prompt_embeds 0.00023597 24.11996922 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.96720829 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.27595112 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.01204853 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00653100 4.80642462 + vae.encoder_f1 0.00653745 4.80588913 + vae.decoder 0.00020026 0.09623204 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 4.10003822 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 78468 +BPFP 0.2776 bits/point +EBPFP 0.5553 equivalent bits/point +MSE 4.100038 +---------------------- -------------------------------------------------------- +Time: 3.731s Load: 0.008s, Pack+Encode: 2.127s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1000 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 392B, BPFP=4.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,620B, BPFP=0.4897 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,920B, BPFP=0.3182 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,240B, BPFP=0.5388 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,556B, BPFP=0.1153 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,556B, BPFP=0.1153 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,688B, BPFP=0.0515 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.94877529 + text_encoder-item0.clip_prompt_embeds 0.00022433 24.17899207 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.96323977 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.27324577 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00997633 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00869686 7.33744669 + vae.encoder_f1 0.00870063 7.33895016 + vae.decoder 0.00021246 0.06088101 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 5.27135955 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80668 +BPFP 0.2854 bits/point +EBPFP 0.5709 equivalent bits/point +MSE 5.271360 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.2714 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 396B, BPFP=4.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,012B, BPFP=0.5427 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,956B, BPFP=0.4023 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,456B, BPFP=0.5696 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,788B, BPFP=0.1188 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,788B, BPFP=0.1188 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,148B, BPFP=0.0656 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.97669188 + text_encoder-item0.clip_prompt_embeds 0.00022433 24.13874586 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.91679993 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.26567811 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00970865 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00626512 6.14828539 + vae.encoder_f1 0.00626949 6.14746189 + vae.decoder 0.00018936 0.07031621 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 4.71898213 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 84244 +BPFP 0.2981 bits/point +EBPFP 0.5962 equivalent bits/point +MSE 4.718982 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.008s, Pack+Encode: 2.128s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7190 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,012B, BPFP=0.5427 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,736B, BPFP=0.4656 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,532B, BPFP=0.5715 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,884B, BPFP=0.1050 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,884B, BPFP=0.1050 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,992B, BPFP=0.0608 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 1.04373590 + text_encoder-item0.clip_prompt_embeds 0.00026137 24.17198069 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.89443398 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.31664159 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00990835 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.35915655 8.26363373 + vae.encoder_f1 0.35915723 8.26258850 + vae.decoder 0.00024181 0.05565530 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 5.70139081 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 83116 +BPFP 0.2941 bits/point +EBPFP 0.5882 equivalent bits/point +MSE 5.701391 +---------------------- -------------------------------------------------------- +Time: 3.739s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.7014 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,220B, BPFP=0.4356 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,320B, BPFP=0.3506 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,088B, BPFP=0.5349 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,052B, BPFP=0.0466 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,052B, BPFP=0.0466 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,112B, BPFP=0.0339 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.99022396 + text_encoder-item0.clip_prompt_embeds 0.00021656 24.17749129 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.96365452 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.25703064 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.01184711 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.29031765 5.63706017 + vae.encoder_f1 0.29031771 5.63705492 + vae.decoder 0.00019965 0.05911309 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 4.48174885 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 70924 +BPFP 0.2509 bits/point +EBPFP 0.5019 equivalent bits/point +MSE 4.481749 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.007s, Pack+Encode: 2.140s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4817 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,480B, BPFP=0.4708 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,780B, BPFP=0.3880 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,868B, BPFP=0.5293 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,164B, BPFP=0.0788 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,164B, BPFP=0.0788 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,892B, BPFP=0.0883 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.98243602 + text_encoder-item0.clip_prompt_embeds 0.00025451 24.12516699 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.89878654 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.26460450 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.01177369 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00595764 2.71336341 + vae.encoder_f1 0.00596395 2.71329355 + vae.decoder 0.00019845 0.12846886 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 3.13276979 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 77428 +BPFP 0.2740 bits/point +EBPFP 0.5479 equivalent bits/point +MSE 3.132770 +---------------------- -------------------------------------------------------- +Time: 3.729s Load: 0.008s, Pack+Encode: 2.128s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1328 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,600B, BPFP=0.4870 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,440B, BPFP=0.4416 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,428B, BPFP=0.5689 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,048B, BPFP=0.0923 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,048B, BPFP=0.0923 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,568B, BPFP=0.0479 +⌛️ [2/4] FRONTEND: Frontend time: 2.121s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.97413627 + text_encoder-item0.clip_prompt_embeds 0.00026157 24.17626953 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.93506718 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.28657381 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.01200903 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.40456498 9.66795921 + vae.encoder_f1 0.40456539 9.66797733 + vae.decoder 0.00020503 0.05276664 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 6.35167772 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80216 +BPFP 0.2838 bits/point +EBPFP 0.5677 equivalent bits/point +MSE 6.351678 +---------------------- -------------------------------------------------------- +Time: 3.717s Load: 0.008s, Pack+Encode: 2.121s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3517 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 376B, BPFP=3.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,184B, BPFP=0.5660 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,100B, BPFP=0.3328 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,580B, BPFP=0.5220 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,912B, BPFP=0.1055 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,912B, BPFP=0.1055 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,512B, BPFP=0.0767 +⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.94801625 + text_encoder-item0.clip_prompt_embeds 0.00027179 24.13834001 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.96691256 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.26443014 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00911699 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00673531 6.90369320 + vae.encoder_f1 0.00673732 6.90369320 + vae.decoder 0.00020129 0.10174251 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 5.07302185 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80276 +BPFP 0.2840 bits/point +EBPFP 0.5681 equivalent bits/point +MSE 5.073022 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.154s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.0730 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,192B, BPFP=0.5671 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,324B, BPFP=0.4321 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,792B, BPFP=0.5781 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,124B, BPFP=0.0934 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,124B, BPFP=0.0934 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,472B, BPFP=0.0449 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.587s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.97805532 + text_encoder-item0.clip_prompt_embeds 0.00023057 24.14864888 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.97835817 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.29856651 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00969472 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00881784 7.19752216 + vae.encoder_f1 0.00882136 7.19769430 + vae.decoder 0.00017598 0.04846763 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 5.20500843 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81092 +BPFP 0.2869 bits/point +EBPFP 0.5739 equivalent bits/point +MSE 5.205008 +---------------------- -------------------------------------------------------- +Time: 3.733s Load: 0.007s, Pack+Encode: 2.140s, Decode+Unpack: 1.587s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.2050 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,180B, BPFP=0.4302 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 592B, BPFP=3.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,916B, BPFP=0.3179 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,076B, BPFP=0.5600 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,244B, BPFP=0.0800 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,244B, BPFP=0.0800 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,332B, BPFP=0.0712 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.581s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.96925656 + text_encoder-item0.clip_prompt_embeds 0.00025208 24.14671689 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.92413445 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.31092278 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00921072 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00582247 2.87002826 + vae.encoder_f1 0.00582996 2.87001085 + vae.decoder 0.00016099 0.09940404 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 3.20430335 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 77088 +BPFP 0.2728 bits/point +EBPFP 0.5455 equivalent bits/point +MSE 3.204303 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.581s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2043 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 376B, BPFP=3.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,404B, BPFP=0.4605 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 596B, BPFP=3.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,172B, BPFP=0.4198 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,460B, BPFP=0.5443 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,100B, BPFP=0.1083 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,100B, BPFP=0.1083 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,776B, BPFP=0.0542 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.587s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.97845586 + text_encoder-item0.clip_prompt_embeds 0.00020809 24.14396053 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.95081921 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.30021483 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.01013761 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00602745 6.73076916 + vae.encoder_f1 0.00603159 6.73078632 + vae.decoder 0.00017526 0.08131354 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 4.99231111 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81100 +BPFP 0.2870 bits/point +EBPFP 0.5739 equivalent bits/point +MSE 4.992311 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.009s, Pack+Encode: 2.142s, Decode+Unpack: 1.587s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.9923 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,364B, BPFP=0.4551 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 600B, BPFP=3.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,488B, BPFP=0.4455 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,388B, BPFP=0.5425 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,872B, BPFP=0.1201 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,864B, BPFP=0.1200 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,420B, BPFP=0.1044 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.587s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.95042785 + text_encoder-item0.clip_prompt_embeds 0.00020908 24.12595331 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.94384518 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.27970044 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.01180029 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00634616 6.50589800 + vae.encoder_f1 0.00635208 6.51646519 + vae.decoder 0.00022721 0.09216511 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 4.89058081 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 84492 +BPFP 0.2990 bits/point +EBPFP 0.5979 equivalent bits/point +MSE 4.890581 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.587s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8906 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,720B, BPFP=0.5032 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,112B, BPFP=0.3338 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,352B, BPFP=0.5416 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,744B, BPFP=0.0724 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,744B, BPFP=0.0724 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,488B, BPFP=0.0454 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.97543661 + text_encoder-item0.clip_prompt_embeds 0.00022947 24.14017265 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 1.03031588 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.27264353 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.01071929 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.05448642 4.57257557 + vae.encoder_f1 0.05448771 4.57258606 + vae.decoder 0.00017748 0.03957417 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 3.98539302 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 75244 +BPFP 0.2662 bits/point +EBPFP 0.5325 equivalent bits/point +MSE 3.985393 +---------------------- -------------------------------------------------------- +Time: 3.724s Load: 0.007s, Pack+Encode: 2.130s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9854 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,024B, BPFP=0.4091 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,244B, BPFP=0.4256 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,420B, BPFP=0.5433 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,768B, BPFP=0.1033 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,772B, BPFP=0.1033 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,836B, BPFP=0.0560 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.585s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.95922645 + text_encoder-item0.clip_prompt_embeds 0.00020169 24.17121339 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.92427931 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.27222938 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.01221111 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.06876971 8.14709091 + vae.encoder_f1 0.06877109 8.17547798 + vae.decoder 0.00023999 0.04139964 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 5.65086736 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80140 +BPFP 0.2836 bits/point +EBPFP 0.5671 equivalent bits/point +MSE 5.650867 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.585s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6509 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 404B, BPFP=4.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,656B, BPFP=0.4946 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 596B, BPFP=3.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,776B, BPFP=0.3065 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,452B, BPFP=0.5441 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,760B, BPFP=0.0726 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,760B, BPFP=0.0726 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,404B, BPFP=0.0734 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.95127519 + text_encoder-item0.clip_prompt_embeds 0.00025253 24.14249146 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.97660885 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.28540099 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.01078810 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00595097 2.56366968 + vae.encoder_f1 0.00595882 2.56342936 + vae.decoder 0.00020134 0.11723247 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 3.06325997 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 75924 +BPFP 0.2686 bits/point +EBPFP 0.5373 equivalent bits/point +MSE 3.063260 +---------------------- -------------------------------------------------------- +Time: 3.747s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0633 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,824B, BPFP=0.5173 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 600B, BPFP=3.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,436B, BPFP=0.3601 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,364B, BPFP=0.5419 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,864B, BPFP=0.0742 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,868B, BPFP=0.0743 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,792B, BPFP=0.0852 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.96323331 + text_encoder-item0.clip_prompt_embeds 0.00022201 24.13651371 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.95058393 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.31551034 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.01204036 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00831743 4.00871325 + vae.encoder_f1 0.00831926 4.00987434 + vae.decoder 0.00028593 0.09777850 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 3.73281471 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 77248 +BPFP 0.2733 bits/point +EBPFP 0.5466 equivalent bits/point +MSE 3.732815 +---------------------- -------------------------------------------------------- +Time: 3.739s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7328 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,988B, BPFP=0.5395 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,268B, BPFP=0.4276 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,656B, BPFP=0.5747 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,568B, BPFP=0.1155 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,564B, BPFP=0.1154 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,712B, BPFP=0.0828 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.587s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.95511007 + text_encoder-item0.clip_prompt_embeds 0.00026808 24.17341171 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.98641386 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.29782747 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.01015500 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00606586 6.03439093 + vae.encoder_f1 0.00607066 6.03482962 + vae.decoder 0.00019664 0.07593013 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 4.66950754 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 84824 +BPFP 0.3001 bits/point +EBPFP 0.6003 equivalent bits/point +MSE 4.669508 +---------------------- -------------------------------------------------------- +Time: 3.733s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.587s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.6695 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 2,984B, BPFP=0.4037 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,820B, BPFP=0.3912 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,756B, BPFP=0.5772 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,968B, BPFP=0.1063 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,960B, BPFP=0.1062 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,748B, BPFP=0.0533 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.583s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.95938007 + text_encoder-item0.clip_prompt_embeds 0.00023198 24.14481027 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 1.01815853 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.31159081 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.01154226 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.05216765 7.95160484 + vae.encoder_f1 0.05216896 7.96446323 + vae.decoder 0.00017960 0.07039291 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 5.56095299 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81304 +BPFP 0.2877 bits/point +EBPFP 0.5754 equivalent bits/point +MSE 5.560953 +---------------------- -------------------------------------------------------- +Time: 3.734s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.583s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.5610 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 392B, BPFP=4.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,612B, BPFP=0.4886 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 604B, BPFP=3.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,160B, BPFP=0.3377 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,520B, BPFP=0.5205 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,540B, BPFP=0.1151 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,540B, BPFP=0.1151 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,676B, BPFP=0.0511 +⌛️ [2/4] FRONTEND: Frontend time: 2.127s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.584s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.97162390 + text_encoder-item0.clip_prompt_embeds 0.00023125 24.17374357 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.99705505 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.27529554 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.01163550 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00620361 5.52033615 + vae.encoder_f1 0.00620966 5.51930571 + vae.decoder 0.00020748 0.08015860 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 4.43049955 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80160 +BPFP 0.2836 bits/point +EBPFP 0.5673 equivalent bits/point +MSE 4.430500 +---------------------- -------------------------------------------------------- +Time: 3.719s Load: 0.008s, Pack+Encode: 2.127s, Decode+Unpack: 1.584s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4305 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,788B, BPFP=0.5124 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,204B, BPFP=0.4224 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,284B, BPFP=0.5399 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,140B, BPFP=0.1089 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,144B, BPFP=0.1090 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,416B, BPFP=0.0737 +⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.586s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.96582389 + text_encoder-item0.clip_prompt_embeds 0.00023066 24.15312585 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.87871065 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.30394767 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.01171645 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.03159856 5.44408846 + vae.encoder_f1 0.03160188 5.43522310 + vae.decoder 0.00018417 0.09091341 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 4.39522050 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 82064 +BPFP 0.2904 bits/point +EBPFP 0.5807 equivalent bits/point +MSE 4.395221 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.009s, Pack+Encode: 2.135s, Decode+Unpack: 1.586s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.3952 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,800B, BPFP=0.5141 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,416B, BPFP=0.3584 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,388B, BPFP=0.5679 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,184B, BPFP=0.2164 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,220B, BPFP=0.2170 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,068B, BPFP=0.0631 +⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.583s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.94024833 + text_encoder-item0.clip_prompt_embeds 0.00024948 24.13725142 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.87916470 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.27680060 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00990191 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.03490865 15.41311550 + vae.encoder_f1 0.03491008 15.52715778 + vae.decoder 0.00028462 0.08123853 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 9.04405587 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 96160 +BPFP 0.3402 bits/point +EBPFP 0.6805 equivalent bits/point +MSE 9.044056 +---------------------- -------------------------------------------------------- +Time: 3.727s Load: 0.009s, Pack+Encode: 2.135s, Decode+Unpack: 1.583s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.0441 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,748B, BPFP=0.5070 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,764B, BPFP=0.4679 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,348B, BPFP=0.5669 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,208B, BPFP=0.0490 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,208B, BPFP=0.0490 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,824B, BPFP=0.0557 +⌛️ [2/4] FRONTEND: Frontend time: 2.125s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.583s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.95092694 + text_encoder-item0.clip_prompt_embeds 0.00021560 24.13799758 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.88915510 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.28563005 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00996447 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00544735 1.65627050 + vae.encoder_f1 0.00544843 1.65626514 + vae.decoder 0.00018632 0.10638367 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 2.64096174 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 75168 +BPFP 0.2660 bits/point +EBPFP 0.5319 equivalent bits/point +MSE 2.640962 +---------------------- -------------------------------------------------------- +Time: 3.717s Load: 0.009s, Pack+Encode: 2.125s, Decode+Unpack: 1.583s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6410 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,520B, BPFP=0.4762 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 568B, BPFP=3.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,188B, BPFP=0.4211 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,456B, BPFP=0.5442 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,344B, BPFP=0.1121 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,348B, BPFP=0.1121 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,368B, BPFP=0.0723 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.581s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.95997572 + text_encoder-item0.clip_prompt_embeds 0.00022698 24.17862005 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.96773348 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.29299903 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.01156755 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00630479 4.77083540 + vae.encoder_f1 0.00631430 4.77235079 + vae.decoder 0.00018596 0.09035213 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 4.08554642 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 82280 +BPFP 0.2911 bits/point +EBPFP 0.5823 equivalent bits/point +MSE 4.085546 +---------------------- -------------------------------------------------------- +Time: 3.733s Load: 0.009s, Pack+Encode: 2.143s, Decode+Unpack: 1.581s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0855 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,208B, BPFP=0.5693 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,220B, BPFP=0.3425 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,072B, BPFP=0.5345 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,752B, BPFP=0.1030 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,756B, BPFP=0.1031 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,304B, BPFP=0.0703 +⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.586s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 1.00735593 + text_encoder-item0.clip_prompt_embeds 0.00024643 24.15340064 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 1.02788153 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.30867658 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.01002570 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00612578 4.12350368 + vae.encoder_f1 0.00613243 4.12557459 + vae.decoder 0.00018179 0.10162801 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 3.78662964 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80388 +BPFP 0.2844 bits/point +EBPFP 0.5689 equivalent bits/point +MSE 3.786630 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.008s, Pack+Encode: 2.136s, Decode+Unpack: 1.586s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7866 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,976B, BPFP=0.5379 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,452B, BPFP=0.4425 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,096B, BPFP=0.5605 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,160B, BPFP=0.0330 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,160B, BPFP=0.0330 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,412B, BPFP=0.0736 +⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.94895299 + text_encoder-item0.clip_prompt_embeds 0.00024049 24.12631899 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.97266550 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.29587642 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.01095127 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00526071 0.76434702 + vae.encoder_f1 0.00526072 0.76434392 + vae.decoder 0.00016981 0.10841784 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 2.22787790 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 73336 +BPFP 0.2595 bits/point +EBPFP 0.5190 equivalent bits/point +MSE 2.227878 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.009s, Pack+Encode: 2.136s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2279 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 376B, BPFP=3.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,552B, BPFP=0.4805 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,788B, BPFP=0.3075 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,440B, BPFP=0.5438 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,424B, BPFP=0.1133 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,424B, BPFP=0.1133 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,832B, BPFP=0.0864 +⌛️ [2/4] FRONTEND: Frontend time: 2.125s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.89194369 + text_encoder-item0.clip_prompt_embeds 0.00022843 24.15569831 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.93505363 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.26063419 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.01143221 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00622977 5.09166050 + vae.encoder_f1 0.00623684 5.09419441 + vae.decoder 0.00019755 0.10067534 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 4.23369712 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81540 +BPFP 0.2885 bits/point +EBPFP 0.5770 equivalent bits/point +MSE 4.233697 +---------------------- -------------------------------------------------------- +Time: 3.727s Load: 0.009s, Pack+Encode: 2.125s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2337 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,032B, BPFP=0.5455 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,216B, BPFP=0.3422 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,760B, BPFP=0.5519 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,240B, BPFP=0.0800 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,236B, BPFP=0.0799 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,436B, BPFP=0.0743 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.586s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.96127407 + text_encoder-item0.clip_prompt_embeds 0.00026004 24.13257153 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 1.05211229 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.26906255 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.01177141 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00725303 3.55454302 + vae.encoder_f1 0.00725507 3.55718231 + vae.decoder 0.00017991 0.06072289 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 3.51612297 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 78004 +BPFP 0.2760 bits/point +EBPFP 0.5520 equivalent bits/point +MSE 3.516123 +---------------------- -------------------------------------------------------- +Time: 3.722s Load: 0.008s, Pack+Encode: 2.128s, Decode+Unpack: 1.586s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5161 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,336B, BPFP=0.5866 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,580B, BPFP=0.3718 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,468B, BPFP=0.5445 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,076B, BPFP=0.1080 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,076B, BPFP=0.1080 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,792B, BPFP=0.0547 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 1.02742020 + text_encoder-item0.clip_prompt_embeds 0.00031748 24.15739778 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 1.04859800 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.28781970 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00842214 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.42111695 8.91638947 + vae.encoder_f1 0.42111716 8.90591717 + vae.decoder 0.00019827 0.05010825 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 5.99952560 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81392 +BPFP 0.2880 bits/point +EBPFP 0.5760 equivalent bits/point +MSE 5.999526 +---------------------- -------------------------------------------------------- +Time: 3.726s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.9995 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,240B, BPFP=0.4383 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,236B, BPFP=0.3438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,860B, BPFP=0.5798 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,816B, BPFP=0.1345 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,808B, BPFP=0.1344 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,164B, BPFP=0.0660 +⌛️ [2/4] FRONTEND: Frontend time: 2.127s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.92983150 + text_encoder-item0.clip_prompt_embeds 0.00024951 24.16093835 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.88890400 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.29964241 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.01059524 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.10376993 10.30218410 + vae.encoder_f1 0.10377157 10.29944420 + vae.decoder 0.00019787 0.05978681 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 6.64591569 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 85216 +BPFP 0.3015 bits/point +EBPFP 0.6030 equivalent bits/point +MSE 6.645916 +---------------------- -------------------------------------------------------- +Time: 3.732s Load: 0.008s, Pack+Encode: 2.127s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.6459 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,416B, BPFP=0.4621 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,876B, BPFP=0.4769 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,208B, BPFP=0.5379 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,312B, BPFP=0.1116 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,312B, BPFP=0.1116 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,792B, BPFP=0.0547 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.96114167 + text_encoder-item0.clip_prompt_embeds 0.00022350 24.15164409 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.90963230 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.30824664 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.01018230 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.01346414 7.21030617 + vae.encoder_f1 0.01346933 7.21060085 + vae.decoder 0.00019243 0.05820503 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 5.21261831 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 82000 +BPFP 0.2901 bits/point +EBPFP 0.5803 equivalent bits/point +MSE 5.212618 +---------------------- -------------------------------------------------------- +Time: 3.733s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.2126 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,460B, BPFP=0.4681 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 568B, BPFP=3.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,120B, BPFP=0.3344 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,280B, BPFP=0.5905 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,096B, BPFP=0.1235 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,100B, BPFP=0.1236 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,744B, BPFP=0.0532 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.94934503 + text_encoder-item0.clip_prompt_embeds 0.00024958 35.17991156 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 1.06550341 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.27788023 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.01005422 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.11196710 9.13459969 + vae.encoder_f1 0.11196851 9.16445351 + vae.decoder 0.00023459 0.05491724 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 6.39870318 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 83864 +BPFP 0.2967 bits/point +EBPFP 0.5935 equivalent bits/point +MSE 6.398703 +---------------------- -------------------------------------------------------- +Time: 3.729s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3987 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,936B, BPFP=0.5325 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 596B, BPFP=3.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,472B, BPFP=0.3630 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,852B, BPFP=0.5289 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,664B, BPFP=0.1169 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,668B, BPFP=0.1170 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,724B, BPFP=0.0526 +⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.96865447 + text_encoder-item0.clip_prompt_embeds 0.00025929 24.16070160 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.97039089 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.31650626 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00858178 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00675017 5.67633343 + vae.encoder_f1 0.00675421 5.67159033 + vae.decoder 0.00023635 0.07392344 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 4.50227554 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81408 +BPFP 0.2880 bits/point +EBPFP 0.5761 equivalent bits/point +MSE 4.502276 +---------------------- -------------------------------------------------------- +Time: 3.735s Load: 0.008s, Pack+Encode: 2.135s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.5023 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,656B, BPFP=0.4946 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,808B, BPFP=0.3903 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,328B, BPFP=0.5664 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,600B, BPFP=0.1312 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,604B, BPFP=0.1313 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,216B, BPFP=0.0676 +⌛️ [2/4] FRONTEND: Frontend time: 2.131s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.93132957 + text_encoder-item0.clip_prompt_embeds 0.00064775 24.17539020 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.94909220 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.28435497 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00963636 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00728993 8.14774799 + vae.encoder_f1 0.00729572 8.13980198 + vae.decoder 0.00026488 0.07145484 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 5.64651492 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 85296 +BPFP 0.3018 bits/point +EBPFP 0.6036 equivalent bits/point +MSE 5.646515 +---------------------- -------------------------------------------------------- +Time: 3.736s Load: 0.008s, Pack+Encode: 2.131s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6465 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,016B, BPFP=0.5433 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,060B, BPFP=0.4107 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,052B, BPFP=0.5594 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,396B, BPFP=0.0976 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,392B, BPFP=0.0975 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,764B, BPFP=0.0538 +⌛️ [2/4] FRONTEND: Frontend time: 2.126s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.98835055 + text_encoder-item0.clip_prompt_embeds 0.00023188 24.15074997 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.94887018 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.27951496 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.01080692 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00613207 5.33283043 + vae.encoder_f1 0.00613899 5.33905888 + vae.decoder 0.00023812 0.07666980 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 4.34426445 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80756 +BPFP 0.2857 bits/point +EBPFP 0.5715 equivalent bits/point +MSE 4.344264 +---------------------- -------------------------------------------------------- +Time: 3.722s Load: 0.008s, Pack+Encode: 2.126s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.3443 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,540B, BPFP=0.4789 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,596B, BPFP=0.4542 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,700B, BPFP=0.5504 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,816B, BPFP=0.1040 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,816B, BPFP=0.1040 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,236B, BPFP=0.0682 +⌛️ [2/4] FRONTEND: Frontend time: 2.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.92564432 + text_encoder-item0.clip_prompt_embeds 0.00023678 24.13347411 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.98479233 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.29839497 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.01202309 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00636537 5.49132109 + vae.encoder_f1 0.00636991 5.49502754 + vae.decoder 0.00025538 0.08133584 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 4.41826339 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81784 +BPFP 0.2894 bits/point +EBPFP 0.5787 equivalent bits/point +MSE 4.418263 +---------------------- -------------------------------------------------------- +Time: 3.732s Load: 0.008s, Pack+Encode: 2.132s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4183 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,136B, BPFP=0.5595 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 592B, BPFP=3.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,416B, BPFP=0.3584 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,436B, BPFP=0.5437 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,560B, BPFP=0.1459 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,560B, BPFP=0.1459 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,628B, BPFP=0.0497 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.96621561 + text_encoder-item0.clip_prompt_embeds 0.00023432 24.15544465 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.82158737 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.24971313 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.01199322 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.23155926 11.17222118 + vae.encoder_f1 0.23156048 11.17229271 + vae.decoder 0.00018572 0.05221728 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 7.04703452 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 85824 +BPFP 0.3037 bits/point +EBPFP 0.6073 equivalent bits/point +MSE 7.047035 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.0470 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,332B, BPFP=0.4508 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 592B, BPFP=3.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,132B, BPFP=0.3354 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,208B, BPFP=0.5379 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,808B, BPFP=0.1191 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,804B, BPFP=0.1191 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,380B, BPFP=0.0726 +⌛️ [2/4] FRONTEND: Frontend time: 2.126s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.96037022 + text_encoder-item0.clip_prompt_embeds 0.00022528 24.16626082 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.96937428 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.26572761 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.01141842 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00729824 6.10242367 + vae.encoder_f1 0.00730369 6.10720778 + vae.decoder 0.00019938 0.09225003 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 4.70254082 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81752 +BPFP 0.2893 bits/point +EBPFP 0.5785 equivalent bits/point +MSE 4.702541 +---------------------- -------------------------------------------------------- +Time: 3.725s Load: 0.008s, Pack+Encode: 2.126s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7025 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,792B, BPFP=0.5130 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,660B, BPFP=0.3782 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,480B, BPFP=0.5195 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,012B, BPFP=0.0765 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,012B, BPFP=0.0765 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,748B, BPFP=0.0839 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.96659390 + text_encoder-item0.clip_prompt_embeds 0.00022149 24.15715681 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.94884129 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.29630470 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.01024016 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00564371 3.10920548 + vae.encoder_f1 0.00565042 3.11011815 + vae.decoder 0.00019980 0.10449638 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 3.31582472 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 76780 +BPFP 0.2717 bits/point +EBPFP 0.5433 equivalent bits/point +MSE 3.315825 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3158 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,524B, BPFP=0.4767 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,180B, BPFP=0.3393 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,640B, BPFP=0.5235 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,580B, BPFP=0.0851 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,580B, BPFP=0.0851 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,732B, BPFP=0.0834 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.93153310 + text_encoder-item0.clip_prompt_embeds 0.00022173 24.15649308 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.93922348 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.32157938 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00851468 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00576096 2.83458281 + vae.encoder_f1 0.00576981 2.83469629 + vae.decoder 0.00019592 0.10638905 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 3.18932396 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 77324 +BPFP 0.2736 bits/point +EBPFP 0.5472 equivalent bits/point +MSE 3.189324 +---------------------- -------------------------------------------------------- +Time: 3.735s Load: 0.009s, Pack+Encode: 2.134s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1893 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 400B, BPFP=4.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,948B, BPFP=0.5341 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,988B, BPFP=0.4049 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,584B, BPFP=0.5982 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,384B, BPFP=0.0669 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,384B, BPFP=0.0669 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,656B, BPFP=0.0505 +⌛️ [2/4] FRONTEND: Frontend time: 2.125s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.97611380 + text_encoder-item0.clip_prompt_embeds 0.00025917 24.17278392 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.97948751 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.30749662 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.01019107 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00594818 3.30464721 + vae.encoder_f1 0.00595328 3.30526686 + vae.decoder 0.00023462 0.06399571 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 3.40261102 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 78040 +BPFP 0.2761 bits/point +EBPFP 0.5523 equivalent bits/point +MSE 3.402611 +---------------------- -------------------------------------------------------- +Time: 3.723s Load: 0.008s, Pack+Encode: 2.125s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4026 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,952B, BPFP=0.5346 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,292B, BPFP=0.4295 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,544B, BPFP=0.5718 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,260B, BPFP=0.1260 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,260B, BPFP=0.1260 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,912B, BPFP=0.0583 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.92722686 + text_encoder-item0.clip_prompt_embeds 0.00022579 24.14732988 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.92799759 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.32195133 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.01057473 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.85445058 9.13271046 + vae.encoder_f1 0.85445166 9.13290882 + vae.decoder 0.00025257 0.03514840 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 6.10201075 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 85284 +BPFP 0.3018 bits/point +EBPFP 0.6035 equivalent bits/point +MSE 6.102011 +---------------------- -------------------------------------------------------- +Time: 3.732s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.1020 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,776B, BPFP=0.5108 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,652B, BPFP=0.3776 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,456B, BPFP=0.5442 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,048B, BPFP=0.1228 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,052B, BPFP=0.1229 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,036B, BPFP=0.0927 +⌛️ [2/4] FRONTEND: Frontend time: 2.126s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.97264632 + text_encoder-item0.clip_prompt_embeds 0.00025458 24.13907561 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.95527134 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.30388049 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00856284 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00628510 5.68247128 + vae.encoder_f1 0.00629234 5.67587757 + vae.decoder 0.00023521 0.11058363 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 4.50781756 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 84104 +BPFP 0.2976 bits/point +EBPFP 0.5952 equivalent bits/point +MSE 4.507818 +---------------------- -------------------------------------------------------- +Time: 3.729s Load: 0.008s, Pack+Encode: 2.126s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.5078 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,300B, BPFP=0.5817 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,388B, BPFP=0.3562 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,100B, BPFP=0.5606 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,524B, BPFP=0.0843 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,524B, BPFP=0.0843 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,960B, BPFP=0.0598 +⌛️ [2/4] FRONTEND: Frontend time: 2.126s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 1.01778857 + text_encoder-item0.clip_prompt_embeds 0.00022807 24.18131933 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 1.05928259 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.26216450 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00985099 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00573429 3.25785160 + vae.encoder_f1 0.00574192 3.25603700 + vae.decoder 0.00017875 0.08471111 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 3.38100507 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 78852 +BPFP 0.2790 bits/point +EBPFP 0.5580 equivalent bits/point +MSE 3.381005 +---------------------- -------------------------------------------------------- +Time: 3.728s Load: 0.009s, Pack+Encode: 2.126s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3810 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,052B, BPFP=0.5482 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,956B, BPFP=0.4834 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,544B, BPFP=0.5465 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,352B, BPFP=0.1122 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,352B, BPFP=0.1122 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,920B, BPFP=0.0891 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.95441008 + text_encoder-item0.clip_prompt_embeds 0.00027120 24.16207555 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.94560156 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.33970258 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.01118011 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00781570 6.64535427 + vae.encoder_f1 0.00781878 6.64542580 + vae.decoder 0.00029724 0.09041872 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 4.95609611 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 84256 +BPFP 0.2981 bits/point +EBPFP 0.5962 equivalent bits/point +MSE 4.956096 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.9561 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,588B, BPFP=0.4854 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 604B, BPFP=3.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,740B, BPFP=0.3847 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,872B, BPFP=0.5548 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,708B, BPFP=0.1024 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,708B, BPFP=0.1024 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,140B, BPFP=0.0958 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.98884956 + text_encoder-item0.clip_prompt_embeds 0.00022930 46.21215080 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.84591675 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.31043457 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.01165429 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00577752 3.47511506 + vae.encoder_f1 0.00578475 3.46991324 + vae.decoder 0.00024190 0.12345528 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 4.06391067 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81864 +BPFP 0.2897 bits/point +EBPFP 0.5793 equivalent bits/point +MSE 4.063911 +---------------------- -------------------------------------------------------- +Time: 3.728s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0639 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,140B, BPFP=0.5601 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,904B, BPFP=0.3169 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,216B, BPFP=0.5889 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,908B, BPFP=0.1512 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,900B, BPFP=0.1511 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,352B, BPFP=0.0413 +⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 1.02349051 + text_encoder-item0.clip_prompt_embeds 0.00028764 24.16832809 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 1.02637243 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.29136158 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.01032913 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.03343784 10.24435043 + vae.encoder_f1 0.03344063 10.24082565 + vae.decoder 0.00016139 0.04162013 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 6.61671081 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 87504 +BPFP 0.3096 bits/point +EBPFP 0.6192 equivalent bits/point +MSE 6.616711 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.009s, Pack+Encode: 2.155s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.6167 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 2,884B, BPFP=0.3902 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,140B, BPFP=0.3360 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,508B, BPFP=0.5456 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,460B, BPFP=0.1291 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,460B, BPFP=0.1291 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,372B, BPFP=0.0724 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.95037746 + text_encoder-item0.clip_prompt_embeds 0.00023094 24.15797061 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.91504002 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.27412335 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.01108877 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00637455 5.95586634 + vae.encoder_f1 0.00637988 5.95685101 + vae.decoder 0.00020059 0.09865063 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 4.63450229 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 82892 +BPFP 0.2933 bits/point +EBPFP 0.5866 equivalent bits/point +MSE 4.634502 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.6345 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,964B, BPFP=0.5363 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 592B, BPFP=3.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,924B, BPFP=0.3997 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,280B, BPFP=0.5651 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,356B, BPFP=0.0970 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,348B, BPFP=0.0969 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,536B, BPFP=0.0774 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 1.02612638 + text_encoder-item0.clip_prompt_embeds 0.00025217 35.17419169 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 1.05486755 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.28089651 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.01011079 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00581597 3.52911067 + vae.encoder_f1 0.00582356 3.52979302 + vae.decoder 0.00019494 0.09564219 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 3.79702327 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81488 +BPFP 0.2883 bits/point +EBPFP 0.5767 equivalent bits/point +MSE 3.797023 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7970 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,112B, BPFP=0.5563 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,020B, BPFP=0.3263 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,276B, BPFP=0.5143 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,572B, BPFP=0.0698 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,572B, BPFP=0.0698 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,916B, BPFP=0.0585 +⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 1.01147318 + text_encoder-item0.clip_prompt_embeds 0.00026975 24.15747176 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.97764053 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.27652287 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.01144176 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 1.11695218 8.75459480 + vae.encoder_f1 1.11695278 8.76026630 + vae.decoder 0.00019720 0.06748445 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 5.93013363 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 74544 +BPFP 0.2638 bits/point +EBPFP 0.5275 equivalent bits/point +MSE 5.930134 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.007s, Pack+Encode: 2.154s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.9301 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,096B, BPFP=0.5541 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 592B, BPFP=3.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,912B, BPFP=0.3175 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,364B, BPFP=0.5673 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,264B, BPFP=0.1108 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,256B, BPFP=0.1107 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,456B, BPFP=0.0750 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.95998669 + text_encoder-item0.clip_prompt_embeds 0.00025545 24.13845627 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.84299898 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.25763054 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.01227235 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.01535016 6.54986954 + vae.encoder_f1 0.01535382 6.56168842 + vae.decoder 0.00021460 0.08730482 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 4.91007710 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 82440 +BPFP 0.2917 bits/point +EBPFP 0.5834 equivalent bits/point +MSE 4.910077 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.9101 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,996B, BPFP=0.5406 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,704B, BPFP=0.4630 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,984B, BPFP=0.5830 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,264B, BPFP=0.0803 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,268B, BPFP=0.0804 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,304B, BPFP=0.0703 +⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.96616332 + text_encoder-item0.clip_prompt_embeds 0.00022628 24.13085092 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.96885643 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.29224776 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.01033304 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00589589 2.72922421 + vae.encoder_f1 0.00590398 2.72745180 + vae.decoder 0.00017838 0.12147257 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 3.14010642 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80584 +BPFP 0.2851 bits/point +EBPFP 0.5703 equivalent bits/point +MSE 3.140106 +---------------------- -------------------------------------------------------- +Time: 3.731s Load: 0.009s, Pack+Encode: 2.135s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1401 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,552B, BPFP=0.4805 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,752B, BPFP=0.3857 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,912B, BPFP=0.5304 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,052B, BPFP=0.1229 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,048B, BPFP=0.1228 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,728B, BPFP=0.0527 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.94329683 + text_encoder-item0.clip_prompt_embeds 0.00031548 24.13910097 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.96054459 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.29402383 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.01144404 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00725484 7.91458797 + vae.encoder_f1 0.00725992 7.91479015 + vae.decoder 0.00019960 0.05961195 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 5.53863413 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 82128 +BPFP 0.2906 bits/point +EBPFP 0.5812 equivalent bits/point +MSE 5.538634 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.5386 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,188B, BPFP=0.5666 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 600B, BPFP=3.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,340B, BPFP=0.3523 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,188B, BPFP=0.5121 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,928B, BPFP=0.1210 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,932B, BPFP=0.1210 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,248B, BPFP=0.0686 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.95821150 + text_encoder-item0.clip_prompt_embeds 0.00021831 24.18370367 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 1.14677715 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.27219215 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00865965 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00923516 6.93028307 + vae.encoder_f1 0.00923823 6.93796730 + vae.decoder 0.00019521 0.06005288 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 5.08386799 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81928 +BPFP 0.2899 bits/point +EBPFP 0.5798 equivalent bits/point +MSE 5.083868 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.009s, Pack+Encode: 2.147s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.0839 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,312B, BPFP=0.4481 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,564B, BPFP=0.3705 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,860B, BPFP=0.5545 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,876B, BPFP=0.1202 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,876B, BPFP=0.1202 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,404B, BPFP=0.0734 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.96280154 + text_encoder-item0.clip_prompt_embeds 0.00062166 24.16884597 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.91867857 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.30200766 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.01208247 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00831779 7.81896353 + vae.encoder_f1 0.00832197 7.81080103 + vae.decoder 0.00023271 0.07497999 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 5.49532671 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 82980 +BPFP 0.2936 bits/point +EBPFP 0.5872 equivalent bits/point +MSE 5.495327 +---------------------- -------------------------------------------------------- +Time: 3.732s Load: 0.008s, Pack+Encode: 2.128s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.4953 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 376B, BPFP=3.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,092B, BPFP=0.5536 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,360B, BPFP=0.3539 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,200B, BPFP=0.5631 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,836B, BPFP=0.0891 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,836B, BPFP=0.0891 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,384B, BPFP=0.0422 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 1.00037177 + text_encoder-item0.clip_prompt_embeds 0.00022938 24.15152995 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.93341351 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.27195645 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00976513 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00626977 4.38248730 + vae.encoder_f1 0.00627489 4.38251734 + vae.decoder 0.00017842 0.06801786 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 3.90062610 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 78780 +BPFP 0.2787 bits/point +EBPFP 0.5575 equivalent bits/point +MSE 3.900626 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.009s, Pack+Encode: 2.150s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9006 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 364B, BPFP=3.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,472B, BPFP=0.4697 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,524B, BPFP=0.3672 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,740B, BPFP=0.5514 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,404B, BPFP=0.0825 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,396B, BPFP=0.0823 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,832B, BPFP=0.0559 +⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.92109386 + text_encoder-item0.clip_prompt_embeds 0.00022180 24.15337316 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.92966967 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.29880153 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00969595 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00585720 3.57751822 + vae.encoder_f1 0.00586586 3.57990003 + vae.decoder 0.00016520 0.10538548 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 3.53336465 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 77436 +BPFP 0.2740 bits/point +EBPFP 0.5480 equivalent bits/point +MSE 3.533365 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.135s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5334 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,172B, BPFP=0.4291 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,772B, BPFP=0.3873 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,520B, BPFP=0.5459 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,180B, BPFP=0.0790 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,180B, BPFP=0.0790 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,684B, BPFP=0.0514 +⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.96322942 + text_encoder-item0.clip_prompt_embeds 0.00025784 24.12292850 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.78232827 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.26830375 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.01088269 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00734802 5.58563185 + vae.encoder_f1 0.00734987 5.58566189 + vae.decoder 0.00018093 0.06228124 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 4.45709151 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 76600 +BPFP 0.2710 bits/point +EBPFP 0.5421 equivalent bits/point +MSE 4.457092 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.008s, Pack+Encode: 2.136s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4571 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 364B, BPFP=3.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,436B, BPFP=0.6001 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,236B, BPFP=0.3438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,600B, BPFP=0.5479 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,096B, BPFP=0.1235 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,096B, BPFP=0.1235 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,800B, BPFP=0.0549 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.99233309 + text_encoder-item0.clip_prompt_embeds 0.00023510 24.17182004 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.90250998 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.27631380 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00939288 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00637359 6.47539616 + vae.encoder_f1 0.00637830 6.47597313 + vae.decoder 0.00018566 0.08386888 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 4.87386360 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 83332 +BPFP 0.2949 bits/point +EBPFP 0.5897 equivalent bits/point +MSE 4.873864 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.009s, Pack+Encode: 2.139s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8739 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,252B, BPFP=0.4399 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 592B, BPFP=3.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,208B, BPFP=0.4227 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,732B, BPFP=0.5512 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,632B, BPFP=0.1012 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,628B, BPFP=0.1011 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,308B, BPFP=0.0399 +⌛️ [2/4] FRONTEND: Frontend time: 2.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.95382945 + text_encoder-item0.clip_prompt_embeds 0.00026418 24.22591146 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.92822065 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.28834538 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.01218202 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.01530954 6.68733883 + vae.encoder_f1 0.01531230 6.68650866 + vae.decoder 0.00017892 0.05056966 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 4.97029855 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 79852 +BPFP 0.2825 bits/point +EBPFP 0.5651 equivalent bits/point +MSE 4.970299 +---------------------- -------------------------------------------------------- +Time: 3.732s Load: 0.008s, Pack+Encode: 2.132s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.9703 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,984B, BPFP=0.5390 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,340B, BPFP=0.3523 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,748B, BPFP=0.5770 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,232B, BPFP=0.0951 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,236B, BPFP=0.0952 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,644B, BPFP=0.0807 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.91811895 + text_encoder-item0.clip_prompt_embeds 0.00021481 24.15174344 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.85924768 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.29697937 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.01000579 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00591154 5.97916651 + vae.encoder_f1 0.00591973 5.97626686 + vae.decoder 0.00025286 0.08810807 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 4.64382498 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81248 +BPFP 0.2875 bits/point +EBPFP 0.5750 equivalent bits/point +MSE 4.643825 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.009s, Pack+Encode: 2.139s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.6438 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,948B, BPFP=0.5341 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,804B, BPFP=0.3899 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,308B, BPFP=0.5912 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,012B, BPFP=0.0765 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,012B, BPFP=0.0765 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,416B, BPFP=0.0737 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.98005207 + text_encoder-item0.clip_prompt_embeds 0.00023458 24.14234350 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.93691788 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.30844245 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.01038304 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00588703 2.35378885 + vae.encoder_f1 0.00589573 2.35293531 + vae.decoder 0.00053402 0.10076332 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 2.96480362 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 79580 +BPFP 0.2816 bits/point +EBPFP 0.5632 equivalent bits/point +MSE 2.964804 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9648 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 392B, BPFP=4.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,176B, BPFP=0.4297 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,268B, BPFP=0.3464 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,416B, BPFP=0.5686 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,968B, BPFP=0.1063 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,980B, BPFP=0.1065 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,872B, BPFP=0.0571 +⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.94054683 + text_encoder-item0.clip_prompt_embeds 0.00022882 24.16442818 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.97531271 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.28244720 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00941696 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00659691 6.09226131 + vae.encoder_f1 0.00660300 6.08515358 + vae.decoder 0.00023739 0.06292050 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 4.69206813 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80768 +BPFP 0.2858 bits/point +EBPFP 0.5716 equivalent bits/point +MSE 4.692068 +---------------------- -------------------------------------------------------- +Time: 3.735s Load: 0.008s, Pack+Encode: 2.136s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.6921 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,556B, BPFP=0.4811 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 604B, BPFP=3.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,176B, BPFP=0.3390 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,752B, BPFP=0.5517 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,612B, BPFP=0.0551 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,612B, BPFP=0.0551 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,304B, BPFP=0.0703 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.96876772 + text_encoder-item0.clip_prompt_embeds 0.00023928 24.16528637 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.95575962 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.28898823 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00933623 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00583864 2.40074611 + vae.encoder_f1 0.00583800 2.40073729 + vae.decoder 0.00018889 0.10966457 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 2.98742164 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 74120 +BPFP 0.2623 bits/point +EBPFP 0.5245 equivalent bits/point +MSE 2.987422 +---------------------- -------------------------------------------------------- +Time: 3.750s Load: 0.007s, Pack+Encode: 2.147s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9874 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,552B, BPFP=0.4805 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 588B, BPFP=3.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,200B, BPFP=0.3409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,552B, BPFP=0.5974 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,844B, BPFP=0.0587 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,844B, BPFP=0.0587 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,112B, BPFP=0.0645 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.97860638 + text_encoder-item0.clip_prompt_embeds 0.00024821 24.12517756 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.77763100 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.28745137 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.01195499 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00570467 1.78178096 + vae.encoder_f1 0.00570488 1.78130674 + vae.decoder 0.00017302 0.08262503 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 2.69627416 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 76196 +BPFP 0.2696 bits/point +EBPFP 0.5392 equivalent bits/point +MSE 2.696274 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6963 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,748B, BPFP=0.5070 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 564B, BPFP=3.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,000B, BPFP=0.3247 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,676B, BPFP=0.5498 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,288B, BPFP=0.0654 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,292B, BPFP=0.0655 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,424B, BPFP=0.0435 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.97391391 + text_encoder-item0.clip_prompt_embeds 0.00021458 24.15819467 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.83255119 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.30696439 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.01200779 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00914783 5.39435387 + vae.encoder_f1 0.00914958 5.39376926 + vae.decoder 0.00017527 0.04661561 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 4.36922072 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 74488 +BPFP 0.2636 bits/point +EBPFP 0.5271 equivalent bits/point +MSE 4.369221 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.3692 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,796B, BPFP=0.5135 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,432B, BPFP=0.3597 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,060B, BPFP=0.5849 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,948B, BPFP=0.0755 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,952B, BPFP=0.0756 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,552B, BPFP=0.0779 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.97272031 + text_encoder-item0.clip_prompt_embeds 0.00022150 24.13124831 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.96863070 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.31229516 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00995455 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00578482 2.38086987 + vae.encoder_f1 0.00579739 2.38033247 + vae.decoder 0.00017668 0.11858881 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 2.97933640 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 78808 +BPFP 0.2788 bits/point +EBPFP 0.5577 equivalent bits/point +MSE 2.979336 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.009s, Pack+Encode: 2.138s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9793 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,360B, BPFP=0.4545 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 600B, BPFP=3.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,872B, BPFP=0.3143 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,400B, BPFP=0.5428 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,072B, BPFP=0.0927 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,076B, BPFP=0.0927 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,984B, BPFP=0.0605 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.97386734 + text_encoder-item0.clip_prompt_embeds 0.00023894 24.14007331 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.94399881 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.27335681 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00921076 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00958025 4.55756855 + vae.encoder_f1 0.00958229 4.55541420 + vae.decoder 0.00019995 0.08479691 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 3.98294313 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 77852 +BPFP 0.2755 bits/point +EBPFP 0.5509 equivalent bits/point +MSE 3.982943 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9829 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,064B, BPFP=0.5498 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 580B, BPFP=3.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,404B, BPFP=0.3575 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,480B, BPFP=0.5702 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,128B, BPFP=0.0477 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,136B, BPFP=0.0479 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,148B, BPFP=0.0656 +⌛️ [2/4] FRONTEND: Frontend time: 2.131s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.94032621 + text_encoder-item0.clip_prompt_embeds 0.00023387 24.15529246 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.97719250 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.30554940 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.01105296 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00567713 1.81585181 + vae.encoder_f1 0.00567905 1.81483209 + vae.decoder 0.00019376 0.12489425 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 2.71840014 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 74444 +BPFP 0.2634 bits/point +EBPFP 0.5268 equivalent bits/point +MSE 2.718400 +---------------------- -------------------------------------------------------- +Time: 3.732s Load: 0.009s, Pack+Encode: 2.131s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7184 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,824B, BPFP=0.5173 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,524B, BPFP=0.3672 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,304B, BPFP=0.5911 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,580B, BPFP=0.0851 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,580B, BPFP=0.0851 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,244B, BPFP=0.0685 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.587s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.93971992 + text_encoder-item0.clip_prompt_embeds 0.00024281 24.15887108 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.92595015 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.28448035 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.01106680 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.02387581 4.48511505 + vae.encoder_f1 0.02387858 4.48227644 + vae.decoder 0.00018648 0.06837785 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 3.94849281 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80144 +BPFP 0.2836 bits/point +EBPFP 0.5671 equivalent bits/point +MSE 3.948493 +---------------------- -------------------------------------------------------- +Time: 3.734s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.587s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9485 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 388B, BPFP=4.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,360B, BPFP=0.4545 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 592B, BPFP=3.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,912B, BPFP=0.3987 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,536B, BPFP=0.5463 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,900B, BPFP=0.1511 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,900B, BPFP=0.1511 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,104B, BPFP=0.0642 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.587s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.96470555 + text_encoder-item0.clip_prompt_embeds 0.00022399 24.15408338 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.88285122 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.28227099 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00890273 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.01169517 9.91565418 + vae.encoder_f1 0.01169969 9.90596390 + vae.decoder 0.00021186 0.05080292 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 6.46283793 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 86808 +BPFP 0.3072 bits/point +EBPFP 0.6143 equivalent bits/point +MSE 6.462838 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.587s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.4628 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,064B, BPFP=0.5498 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 592B, BPFP=3.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,232B, BPFP=0.3435 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,252B, BPFP=0.5898 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,872B, BPFP=0.1812 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,872B, BPFP=0.1812 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,780B, BPFP=0.1154 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.93982601 + text_encoder-item0.clip_prompt_embeds 0.00022123 24.13732752 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.89572620 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.27945576 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.01033183 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.32749966 17.18148804 + vae.encoder_f1 0.32750070 17.18149376 + vae.decoder 0.00039956 0.09390083 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 9.83938244 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 94164 +BPFP 0.3332 bits/point +EBPFP 0.6664 equivalent bits/point +MSE 9.839382 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.8394 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,464B, BPFP=0.4686 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,420B, BPFP=0.3588 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,344B, BPFP=0.5414 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,104B, BPFP=0.0779 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,096B, BPFP=0.0778 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,220B, BPFP=0.0677 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.93710287 + text_encoder-item0.clip_prompt_embeds 0.00024675 24.14999324 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.86483965 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.28546594 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00931416 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00566967 2.96904898 + vae.encoder_f1 0.00567867 2.96605110 + vae.decoder 0.00017839 0.09390683 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 3.24784346 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 76716 +BPFP 0.2714 bits/point +EBPFP 0.5429 equivalent bits/point +MSE 3.247843 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2478 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 380B, BPFP=3.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,404B, BPFP=0.4605 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 576B, BPFP=3.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,484B, BPFP=0.2828 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,692B, BPFP=0.5502 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,324B, BPFP=0.0507 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,324B, BPFP=0.0507 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,728B, BPFP=0.0527 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.587s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.93948046 + text_encoder-item0.clip_prompt_embeds 0.00022364 24.17564597 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 1.04120474 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.27518049 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.01033872 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00580750 2.24277925 + vae.encoder_f1 0.00580664 2.24277520 + vae.decoder 0.00018044 0.12620236 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 2.91592750 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 72028 +BPFP 0.2549 bits/point +EBPFP 0.5097 equivalent bits/point +MSE 2.915927 +---------------------- -------------------------------------------------------- +Time: 3.734s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.587s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9159 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 372B, BPFP=3.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,700B, BPFP=0.5005 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,108B, BPFP=0.3334 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,068B, BPFP=0.5598 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,020B, BPFP=0.1071 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,020B, BPFP=0.1071 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,628B, BPFP=0.0497 +⌛️ [2/4] FRONTEND: Frontend time: 2.126s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.95873952 + text_encoder-item0.clip_prompt_embeds 0.00030118 24.18624231 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.85420990 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.26169961 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.01194719 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.03869025 6.47439384 + vae.encoder_f1 0.03869358 6.47456455 + vae.decoder 0.00021614 0.06178448 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 4.87080175 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 80616 +BPFP 0.2852 bits/point +EBPFP 0.5705 equivalent bits/point +MSE 4.870802 +---------------------- -------------------------------------------------------- +Time: 3.725s Load: 0.008s, Pack+Encode: 2.126s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8708 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 392B, BPFP=4.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,800B, BPFP=0.5141 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 584B, BPFP=3.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 5,316B, BPFP=0.4315 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,796B, BPFP=0.5529 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,392B, BPFP=0.1433 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,380B, BPFP=0.1431 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,708B, BPFP=0.0521 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.95586721 + text_encoder-item0.clip_prompt_embeds 0.00023260 35.14480181 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 1.00513020 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.28820478 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.01158732 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00839879 9.46686268 + vae.encoder_f1 0.00840224 9.43346596 + vae.decoder 0.00019463 0.05594161 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 6.53796180 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 86484 +BPFP 0.3060 bits/point +EBPFP 0.6120 equivalent bits/point +MSE 6.537962 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.5380 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 384B, BPFP=4.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,904B, BPFP=0.5281 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 568B, BPFP=3.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,548B, BPFP=0.3692 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,408B, BPFP=0.5430 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,684B, BPFP=0.1325 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,680B, BPFP=0.1324 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,072B, BPFP=0.0632 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.95738284 + text_encoder-item0.clip_prompt_embeds 0.00023544 24.11800342 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.96476297 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.29987732 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00905925 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.01160815 8.07672787 + vae.encoder_f1 0.01161249 8.07806015 + vae.decoder 0.00021720 0.06268868 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 5.61382609 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 84364 +BPFP 0.2985 bits/point +EBPFP 0.5970 equivalent bits/point +MSE 5.613826 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.009s, Pack+Encode: 2.147s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6138 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,724B, BPFP=0.5038 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 596B, BPFP=3.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 4,740B, BPFP=0.3847 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,856B, BPFP=0.5544 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 5,380B, BPFP=0.0821 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 5,384B, BPFP=0.0822 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,208B, BPFP=0.0674 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.94985604 + text_encoder-item0.clip_prompt_embeds 0.00022923 24.15210067 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.94070024 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.27212120 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.01161925 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.02989292 6.11934137 + vae.encoder_f1 0.02989391 6.11396694 + vae.decoder 0.00034944 0.05734404 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 4.70390060 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 78372 +BPFP 0.2773 bits/point +EBPFP 0.5546 equivalent bits/point +MSE 4.703901 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7039 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 376B, BPFP=3.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 3,876B, BPFP=0.5244 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 560B, BPFP=3.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 3,576B, BPFP=0.2903 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,092B, BPFP=0.5604 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 368B, BPFP=3.8333 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,228B, BPFP=0.5720 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 572B, BPFP=3.5750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 7,016B, BPFP=0.5695 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 21,932B, BPFP=0.5563 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,316B, BPFP=0.1116 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,316B, BPFP=0.1116 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,824B, BPFP=0.0557 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.99389807 + text_encoder-item0.clip_prompt_embeds 0.00024627 24.18461259 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 1.01237488 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.30276398 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.01228809 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 + text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 + vae.encoder_f0 0.00613025 4.50184011 + vae.encoder_f1 0.00613536 4.50274754 + vae.decoder 0.00018697 0.08069747 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 3.96025434 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 81052 +BPFP 0.2868 bits/point +EBPFP 0.5736 equivalent bits/point +MSE 3.960254 +---------------------- -------------------------------------------------------- +Time: 3.747s Load: 0.009s, Pack+Encode: 2.140s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9603 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.2846 bits/point +Avg EBPFP 0.5691 equivalent bits/point +Avg MSE 4.583636 +Avg Time 3.748s +------------------------ ---------------------------- diff --git a/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log b/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..536c39a890f3723dc749b4767a6cfa3e6e6a770b --- /dev/null +++ b/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log @@ -0,0 +1,21862 @@ +Experiment: dtufc_hyperprior-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: sd35 + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 405 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,160B, BPFP=0.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,712B, BPFP=0.1390 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,848B, BPFP=0.1483 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,804B, BPFP=0.0580 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,804B, BPFP=0.0580 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,692B, BPFP=0.0516 +⌛️ [2/4] FRONTEND: Frontend time: 0.689s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 5.86006737 + text_encoder-item0.clip_prompt_embeds 0.00025464 24.24851825 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 6.70058670 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.33525506 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.01759941 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00635250 10.32003784 + vae.encoder_f1 0.00635834 10.32043362 + vae.decoder 0.00019940 0.11003008 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 6.10611106 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28648 +BPFP 0.1014 bits/point +EBPFP 0.2027 equivalent bits/point +MSE 6.106111 +---------------------- -------------------------------------------------------- +Time: 1.204s Load: 0.009s, Pack+Encode: 0.689s, Decode+Unpack: 0.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.1061 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,160B, BPFP=0.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,756B, BPFP=0.1425 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,792B, BPFP=0.1469 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,532B, BPFP=0.0539 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,532B, BPFP=0.0539 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,804B, BPFP=0.0551 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 5.73184776 + text_encoder-item0.clip_prompt_embeds 0.00022609 24.26688269 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 6.64336166 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.36421350 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.01646706 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.01130640 13.92749500 + vae.encoder_f1 0.01130902 13.92757988 + vae.decoder 0.00020860 0.09440794 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 7.77876008 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28200 +BPFP 0.0998 bits/point +EBPFP 0.1996 equivalent bits/point +MSE 7.778760 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.7788 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,156B, BPFP=0.1564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,796B, BPFP=0.1458 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,784B, BPFP=0.1467 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,732B, BPFP=0.0417 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,732B, BPFP=0.0417 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,624B, BPFP=0.0496 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 5.80968539 + text_encoder-item0.clip_prompt_embeds 0.00022402 24.29062035 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 6.96752014 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.34797577 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.01556992 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 1.19630027 25.22118378 + vae.encoder_f1 1.19630098 25.22137260 + vae.decoder 0.00023596 0.07733092 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 13.01445484 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26448 +BPFP 0.0936 bits/point +EBPFP 0.1872 equivalent bits/point +MSE 13.014455 +---------------------- -------------------------------------------------------- +Time: 0.733s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 13.0145 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,160B, BPFP=0.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,808B, BPFP=0.1468 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,696B, BPFP=0.1445 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,404B, BPFP=0.0519 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,404B, BPFP=0.0519 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,720B, BPFP=0.0525 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.436s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 5.80159823 + text_encoder-item0.clip_prompt_embeds 0.00030342 24.27711758 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 6.74976425 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.37914258 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.01494916 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00586287 5.73042440 + vae.encoder_f1 0.00587438 5.72962141 + vae.decoder 0.00017677 0.13959241 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 3.98304365 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27816 +BPFP 0.0984 bits/point +EBPFP 0.1968 equivalent bits/point +MSE 3.983044 +---------------------- -------------------------------------------------------- +Time: 0.732s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.436s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9830 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,152B, BPFP=0.1558 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,800B, BPFP=0.1461 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,588B, BPFP=0.1417 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,248B, BPFP=0.0496 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,248B, BPFP=0.0496 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,632B, BPFP=0.0498 +⌛️ [2/4] FRONTEND: Frontend time: 0.284s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 5.73020363 + text_encoder-item0.clip_prompt_embeds 0.00024120 24.29320126 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 6.85629501 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.34332291 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.01456944 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00779453 9.53907871 + vae.encoder_f1 0.00779802 9.53951645 + vae.decoder 0.00023829 0.10163735 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 5.74410553 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27296 +BPFP 0.0966 bits/point +EBPFP 0.1932 equivalent bits/point +MSE 5.744106 +---------------------- -------------------------------------------------------- +Time: 0.731s Load: 0.008s, Pack+Encode: 0.284s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.7441 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,164B, BPFP=0.1575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,804B, BPFP=0.1464 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,712B, BPFP=0.1449 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,084B, BPFP=0.0623 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,084B, BPFP=0.0623 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,828B, BPFP=0.0558 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.441s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 5.89009857 + text_encoder-item0.clip_prompt_embeds 0.00025651 24.30007525 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 6.74539566 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.36501088 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.01523677 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00655775 13.23353004 + vae.encoder_f1 0.00656268 13.23363495 + vae.decoder 0.00020283 0.09907825 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 7.45831019 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29304 +BPFP 0.1037 bits/point +EBPFP 0.2074 equivalent bits/point +MSE 7.458310 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.441s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.4583 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,180B, BPFP=0.1596 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,796B, BPFP=0.1458 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,708B, BPFP=0.1448 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,692B, BPFP=0.0563 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,692B, BPFP=0.0563 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,716B, BPFP=0.0524 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 5.81368510 + text_encoder-item0.clip_prompt_embeds 0.00022242 24.26551086 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 6.84268799 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.31265645 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.01434100 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00593415 8.52123642 + vae.encoder_f1 0.00594307 8.52116108 + vae.decoder 0.00018992 0.14073795 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 5.27440491 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28412 +BPFP 0.1005 bits/point +EBPFP 0.2011 equivalent bits/point +MSE 5.274405 +---------------------- -------------------------------------------------------- +Time: 0.734s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.2744 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,124B, BPFP=0.1521 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,760B, BPFP=0.1429 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,740B, BPFP=0.1456 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,184B, BPFP=0.0638 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,184B, BPFP=0.0638 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,924B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 5.67496808 + text_encoder-item0.clip_prompt_embeds 0.00022110 24.26325335 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 6.80705872 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.36548779 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.01551051 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00641770 11.17950535 + vae.encoder_f1 0.00642053 11.17953300 + vae.decoder 0.00017498 0.09442361 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 6.50421918 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29544 +BPFP 0.1045 bits/point +EBPFP 0.2091 equivalent bits/point +MSE 6.504219 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.008s, Pack+Encode: 0.287s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.5042 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,112B, BPFP=0.1504 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,804B, BPFP=0.1464 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,808B, BPFP=0.1473 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,256B, BPFP=0.0497 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,256B, BPFP=0.0497 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,608B, BPFP=0.0491 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 5.84037908 + text_encoder-item0.clip_prompt_embeds 0.00021654 24.22946682 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 6.79808960 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.35467767 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.01640833 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00577698 5.87507629 + vae.encoder_f1 0.00578348 5.87477732 + vae.decoder 0.00017559 0.11333553 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 4.04513250 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27468 +BPFP 0.0972 bits/point +EBPFP 0.1944 equivalent bits/point +MSE 4.045132 +---------------------- -------------------------------------------------------- +Time: 0.736s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0451 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,140B, BPFP=0.1542 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,788B, BPFP=0.1451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,828B, BPFP=0.1478 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,692B, BPFP=0.0563 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,692B, BPFP=0.0563 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,636B, BPFP=0.0499 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 5.77768644 + text_encoder-item0.clip_prompt_embeds 0.00022160 24.23789443 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 6.66585083 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.33441503 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.01621516 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00668450 9.96721363 + vae.encoder_f1 0.00668875 9.96726227 + vae.decoder 0.00023059 0.08809385 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 5.93930339 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28400 +BPFP 0.1005 bits/point +EBPFP 0.2010 equivalent bits/point +MSE 5.939303 +---------------------- -------------------------------------------------------- +Time: 0.733s Load: 0.009s, Pack+Encode: 0.286s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.9393 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,180B, BPFP=0.1596 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,760B, BPFP=0.1429 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,816B, BPFP=0.1475 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,360B, BPFP=0.0665 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,360B, BPFP=0.0665 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,584B, BPFP=0.0483 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.437s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 5.75308673 + text_encoder-item0.clip_prompt_embeds 0.00023190 24.28457074 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 6.45545959 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.34934756 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.01815756 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.04018118 18.53036118 + vae.encoder_f1 0.04018488 18.53047180 + vae.decoder 0.00016201 0.05558336 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 9.90887847 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29688 +BPFP 0.1050 bits/point +EBPFP 0.2101 equivalent bits/point +MSE 9.908878 +---------------------- -------------------------------------------------------- +Time: 0.734s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.437s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.9089 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,152B, BPFP=0.1558 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,788B, BPFP=0.1451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,880B, BPFP=0.1491 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,784B, BPFP=0.0577 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,784B, BPFP=0.0577 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,012B, BPFP=0.0614 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 5.80686315 + text_encoder-item0.clip_prompt_embeds 0.00023140 24.24565409 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 6.62382355 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.33632646 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.01674527 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.04874706 15.98294830 + vae.encoder_f1 0.04875064 15.98247623 + vae.decoder 0.00019641 0.11820547 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 8.73292613 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29028 +BPFP 0.1027 bits/point +EBPFP 0.2054 equivalent bits/point +MSE 8.732926 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.7329 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,172B, BPFP=0.1585 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,780B, BPFP=0.1445 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,760B, BPFP=0.1461 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,264B, BPFP=0.0651 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,264B, BPFP=0.0651 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,424B, BPFP=0.0435 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 5.83081690 + text_encoder-item0.clip_prompt_embeds 0.00030893 24.23616959 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 6.77332153 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.33635401 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.01488828 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.01360236 17.57294273 + vae.encoder_f1 0.01360807 17.57323265 + vae.decoder 0.00023006 0.06079565 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 9.46342220 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29288 +BPFP 0.1036 bits/point +EBPFP 0.2073 equivalent bits/point +MSE 9.463422 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.4634 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,168B, BPFP=0.1580 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,708B, BPFP=0.1386 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,728B, BPFP=0.1453 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,588B, BPFP=0.0395 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,588B, BPFP=0.0395 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,128B, BPFP=0.0344 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 5.73277664 + text_encoder-item0.clip_prompt_embeds 0.00024198 24.30014500 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 6.64869232 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.35029891 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.01540800 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 1.67190456 22.64357185 + vae.encoder_f1 1.67190480 22.64355278 + vae.decoder 0.00017417 0.03737578 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 11.81448111 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 25536 +BPFP 0.0904 bits/point +EBPFP 0.1807 equivalent bits/point +MSE 11.814481 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.007s, Pack+Encode: 0.292s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.8145 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,136B, BPFP=0.1537 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,756B, BPFP=0.1425 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,816B, BPFP=0.1475 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,680B, BPFP=0.0562 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,680B, BPFP=0.0562 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,848B, BPFP=0.0564 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 5.67854945 + text_encoder-item0.clip_prompt_embeds 0.00025129 24.21892333 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 6.64941101 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.33554695 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.01802980 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00621760 10.76399994 + vae.encoder_f1 0.00622505 10.76364708 + vae.decoder 0.00025114 0.11618795 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 6.31175497 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28540 +BPFP 0.1010 bits/point +EBPFP 0.2020 equivalent bits/point +MSE 6.311755 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3118 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,132B, BPFP=0.1531 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,776B, BPFP=0.1442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,768B, BPFP=0.1463 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,212B, BPFP=0.0643 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,212B, BPFP=0.0643 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,884B, BPFP=0.0575 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 5.72483126 + text_encoder-item0.clip_prompt_embeds 0.00020838 24.21268559 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 6.86829987 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.35138891 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.01501142 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00675961 16.59027481 + vae.encoder_f1 0.00676652 16.59030342 + vae.decoder 0.00021373 0.11403565 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 9.01388043 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29604 +BPFP 0.1047 bits/point +EBPFP 0.2095 equivalent bits/point +MSE 9.013880 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.0139 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,116B, BPFP=0.1510 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,772B, BPFP=0.1438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,740B, BPFP=0.1456 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,832B, BPFP=0.0432 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,832B, BPFP=0.0432 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,860B, BPFP=0.0568 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 5.79852994 + text_encoder-item0.clip_prompt_embeds 0.00021387 24.22172619 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 6.71812057 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.34315594 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.01560721 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00596338 2.70486474 + vae.encoder_f1 0.00596322 2.70502949 + vae.decoder 0.00018207 0.16911782 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 2.58058856 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26780 +BPFP 0.0948 bits/point +EBPFP 0.1895 equivalent bits/point +MSE 2.580589 +---------------------- -------------------------------------------------------- +Time: 0.734s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5806 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,148B, BPFP=0.1553 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,804B, BPFP=0.1464 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,696B, BPFP=0.1445 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,760B, BPFP=0.0421 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,760B, BPFP=0.0421 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,676B, BPFP=0.0511 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 5.75993729 + text_encoder-item0.clip_prompt_embeds 0.00022138 24.25013317 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 6.61872940 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.36440533 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.01567535 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00552804 2.02594352 + vae.encoder_f1 0.00552758 2.02592969 + vae.decoder 0.00018040 0.11979313 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 2.26157573 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26468 +BPFP 0.0937 bits/point +EBPFP 0.1873 equivalent bits/point +MSE 2.261576 +---------------------- -------------------------------------------------------- +Time: 0.735s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2616 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,172B, BPFP=0.1585 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,808B, BPFP=0.1468 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,712B, BPFP=0.1449 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,448B, BPFP=0.0526 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,448B, BPFP=0.0526 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,556B, BPFP=0.0475 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 5.78769811 + text_encoder-item0.clip_prompt_embeds 0.00024507 24.30176204 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 6.77837677 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.35520475 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.01499834 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00721525 6.27175999 + vae.encoder_f1 0.00721777 6.27203751 + vae.decoder 0.00018707 0.07173294 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 4.22610005 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27772 +BPFP 0.0983 bits/point +EBPFP 0.1965 equivalent bits/point +MSE 4.226100 +---------------------- -------------------------------------------------------- +Time: 0.734s Load: 0.008s, Pack+Encode: 0.287s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2261 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,188B, BPFP=0.1607 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,784B, BPFP=0.1448 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,792B, BPFP=0.1469 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,888B, BPFP=0.0593 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,888B, BPFP=0.0593 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,084B, BPFP=0.0636 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 5.78469404 + text_encoder-item0.clip_prompt_embeds 0.00046272 24.28726791 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 6.71037292 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.32473477 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.01483673 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.01999603 15.68627548 + vae.encoder_f1 0.01999529 15.68629646 + vae.decoder 0.00024882 0.17277801 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 8.60213866 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29252 +BPFP 0.1035 bits/point +EBPFP 0.2070 equivalent bits/point +MSE 8.602139 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.6021 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,116B, BPFP=0.1510 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,772B, BPFP=0.1438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,752B, BPFP=0.1459 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,428B, BPFP=0.0676 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,432B, BPFP=0.0676 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,728B, BPFP=0.0527 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 5.72532527 + text_encoder-item0.clip_prompt_embeds 0.00020334 24.22732346 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 6.62194061 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.33841323 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.01502965 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.01341345 16.12368202 + vae.encoder_f1 0.01341645 16.12168312 + vae.decoder 0.00018350 0.06255352 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 8.79073094 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29856 +BPFP 0.1056 bits/point +EBPFP 0.2113 equivalent bits/point +MSE 8.790731 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.7907 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,108B, BPFP=0.1499 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,740B, BPFP=0.1412 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,828B, BPFP=0.1478 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,716B, BPFP=0.0567 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,716B, BPFP=0.0567 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,004B, BPFP=0.0612 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 5.65436681 + text_encoder-item0.clip_prompt_embeds 0.00022316 24.23357599 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 6.63369293 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.32499552 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.01593526 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00606298 8.80464172 + vae.encoder_f1 0.00607096 8.80479431 + vae.decoder 0.00023408 0.15547945 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 5.40735384 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28740 +BPFP 0.1017 bits/point +EBPFP 0.2034 equivalent bits/point +MSE 5.407354 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.4074 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,144B, BPFP=0.1548 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,808B, BPFP=0.1468 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,756B, BPFP=0.1460 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,560B, BPFP=0.0543 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,560B, BPFP=0.0543 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,876B, BPFP=0.0573 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 5.70303663 + text_encoder-item0.clip_prompt_embeds 0.00023597 24.21878171 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 6.67678604 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.33227925 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.01575981 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00653100 9.49621773 + vae.encoder_f1 0.00653745 9.49628830 + vae.decoder 0.00020026 0.15332761 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 5.72776326 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28328 +BPFP 0.1002 bits/point +EBPFP 0.2005 equivalent bits/point +MSE 5.727763 +---------------------- -------------------------------------------------------- +Time: 0.736s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.7278 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,160B, BPFP=0.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,756B, BPFP=0.1425 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,664B, BPFP=0.1437 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,816B, BPFP=0.0582 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,816B, BPFP=0.0582 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,608B, BPFP=0.0491 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.436s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 5.77378654 + text_encoder-item0.clip_prompt_embeds 0.00022433 24.28119927 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 6.64011230 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.32889281 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.01399550 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00869686 17.55913734 + vae.encoder_f1 0.00870063 17.55661964 + vae.decoder 0.00021246 0.08941051 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 9.46031951 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28448 +BPFP 0.1007 bits/point +EBPFP 0.2013 equivalent bits/point +MSE 9.460320 +---------------------- -------------------------------------------------------- +Time: 0.731s Load: 0.009s, Pack+Encode: 0.286s, Decode+Unpack: 0.436s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.4603 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,140B, BPFP=0.1542 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,804B, BPFP=0.1464 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,704B, BPFP=0.1447 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,100B, BPFP=0.0626 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,100B, BPFP=0.0626 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,780B, BPFP=0.0543 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 5.76268005 + text_encoder-item0.clip_prompt_embeds 0.00022433 24.23726030 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 6.47898865 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.33261750 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.01589870 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00626512 14.28665638 + vae.encoder_f1 0.00626949 14.28564835 + vae.decoder 0.00018936 0.08867904 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 7.94209607 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29252 +BPFP 0.1035 bits/point +EBPFP 0.2070 equivalent bits/point +MSE 7.942096 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.9421 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,148B, BPFP=0.1553 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,784B, BPFP=0.1448 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,740B, BPFP=0.1456 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,488B, BPFP=0.0532 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,488B, BPFP=0.0532 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,720B, BPFP=0.0525 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 5.82656924 + text_encoder-item0.clip_prompt_embeds 0.00026137 24.27695904 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 6.77076492 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.37919054 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.01603456 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.35915655 22.45770264 + vae.encoder_f1 0.35915723 22.45785332 + vae.decoder 0.00024181 0.07755669 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 11.73382028 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27996 +BPFP 0.0991 bits/point +EBPFP 0.1981 equivalent bits/point +MSE 11.733820 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.7338 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,144B, BPFP=0.1548 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,796B, BPFP=0.1458 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,736B, BPFP=0.1455 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,804B, BPFP=0.0428 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,804B, BPFP=0.0428 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,316B, BPFP=0.0402 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 5.79262288 + text_encoder-item0.clip_prompt_embeds 0.00021656 24.26642823 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 6.82602158 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.30747498 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.01480578 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.29031765 13.24293518 + vae.encoder_f1 0.29031771 13.23397732 + vae.decoder 0.00019965 0.07199308 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 7.45399448 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26228 +BPFP 0.0928 bits/point +EBPFP 0.1856 equivalent bits/point +MSE 7.453994 +---------------------- -------------------------------------------------------- +Time: 0.735s Load: 0.007s, Pack+Encode: 0.288s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.4540 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,148B, BPFP=0.1553 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,776B, BPFP=0.1442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,804B, BPFP=0.1472 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,212B, BPFP=0.0490 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,212B, BPFP=0.0490 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,964B, BPFP=0.0599 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 5.75286674 + text_encoder-item0.clip_prompt_embeds 0.00025451 24.25854809 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 6.65558090 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.32567329 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.01502750 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00595764 5.03496361 + vae.encoder_f1 0.00596395 5.03494978 + vae.decoder 0.00019845 0.16714205 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 3.66101283 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27740 +BPFP 0.0982 bits/point +EBPFP 0.1963 equivalent bits/point +MSE 3.661013 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6610 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,128B, BPFP=0.1526 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,784B, BPFP=0.1448 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,708B, BPFP=0.1448 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,068B, BPFP=0.0468 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,068B, BPFP=0.0468 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,540B, BPFP=0.0470 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 5.81032181 + text_encoder-item0.clip_prompt_embeds 0.00026157 24.28919144 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 6.53664017 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.38100309 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.01549622 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.40456498 24.28011131 + vae.encoder_f1 0.40456539 24.28112411 + vae.decoder 0.00020503 0.07510839 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 12.57909716 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26924 +BPFP 0.0953 bits/point +EBPFP 0.1905 equivalent bits/point +MSE 12.579097 +---------------------- -------------------------------------------------------- +Time: 0.735s Load: 0.008s, Pack+Encode: 0.287s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 12.5791 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,220B, BPFP=0.1650 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,792B, BPFP=0.1455 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,732B, BPFP=0.1454 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,712B, BPFP=0.0566 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,712B, BPFP=0.0566 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,876B, BPFP=0.0573 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 5.73178164 + text_encoder-item0.clip_prompt_embeds 0.00027179 24.33190442 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 6.81530380 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.33092499 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.01444135 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00673531 15.73708248 + vae.encoder_f1 0.00673732 15.73717880 + vae.decoder 0.00020129 0.13154639 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 8.62236190 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28668 +BPFP 0.1014 bits/point +EBPFP 0.2029 equivalent bits/point +MSE 8.622362 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.6224 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,156B, BPFP=0.1564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,784B, BPFP=0.1448 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,740B, BPFP=0.1456 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,820B, BPFP=0.0583 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,820B, BPFP=0.0583 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,496B, BPFP=0.0457 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 5.73641713 + text_encoder-item0.clip_prompt_embeds 0.00023057 24.31095272 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 6.89126434 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.36568219 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.01676697 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00881784 16.84390831 + vae.encoder_f1 0.00882136 16.84406090 + vae.decoder 0.00017598 0.06191485 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 9.12894837 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28444 +BPFP 0.1006 bits/point +EBPFP 0.2013 equivalent bits/point +MSE 9.128948 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.1289 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,140B, BPFP=0.1542 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,776B, BPFP=0.1442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,808B, BPFP=0.1473 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,316B, BPFP=0.0506 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,316B, BPFP=0.0506 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,676B, BPFP=0.0511 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 5.78399531 + text_encoder-item0.clip_prompt_embeds 0.00025208 24.21988721 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 6.77946625 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.35630196 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.01696303 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00582247 4.60144949 + vae.encoder_f1 0.00582996 4.60147047 + vae.decoder 0.00016099 0.12761387 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 3.45606258 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27656 +BPFP 0.0979 bits/point +EBPFP 0.1957 equivalent bits/point +MSE 3.456063 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4561 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,148B, BPFP=0.1553 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,816B, BPFP=0.1474 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,636B, BPFP=0.1430 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,852B, BPFP=0.0588 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,852B, BPFP=0.0588 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,592B, BPFP=0.0486 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 5.70345751 + text_encoder-item0.clip_prompt_embeds 0.00020809 24.24753957 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 6.70116501 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.37432845 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.01420272 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00602745 13.84544659 + vae.encoder_f1 0.00603159 13.84454250 + vae.decoder 0.00017526 0.10578810 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 7.74144098 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28520 +BPFP 0.1009 bits/point +EBPFP 0.2018 equivalent bits/point +MSE 7.741441 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.7414 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,164B, BPFP=0.1575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,772B, BPFP=0.1438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,852B, BPFP=0.1484 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,920B, BPFP=0.0598 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,920B, BPFP=0.0598 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,104B, BPFP=0.0642 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 5.65776443 + text_encoder-item0.clip_prompt_embeds 0.00020908 24.22381037 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 6.84566193 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.33021371 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.01576852 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00634616 14.10025311 + vae.encoder_f1 0.00635208 14.09898567 + vae.decoder 0.00022721 0.15145659 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 7.86256380 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29360 +BPFP 0.1039 bits/point +EBPFP 0.2078 equivalent bits/point +MSE 7.862564 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.8626 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,144B, BPFP=0.1548 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,788B, BPFP=0.1451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,776B, BPFP=0.1465 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,292B, BPFP=0.0502 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,292B, BPFP=0.0502 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,516B, BPFP=0.0463 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 5.77422460 + text_encoder-item0.clip_prompt_embeds 0.00022947 24.26724626 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 6.78494339 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.32494015 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.01518336 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.05448642 12.44151497 + vae.encoder_f1 0.05448771 12.44155121 + vae.decoder 0.00017748 0.05254446 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 7.08295791 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27432 +BPFP 0.0971 bits/point +EBPFP 0.1941 equivalent bits/point +MSE 7.082958 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.0830 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,104B, BPFP=0.1494 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,800B, BPFP=0.1461 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,820B, BPFP=0.1476 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,068B, BPFP=0.0621 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,068B, BPFP=0.0621 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,828B, BPFP=0.0558 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 5.74806658 + text_encoder-item0.clip_prompt_embeds 0.00020169 24.22666819 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 6.71879044 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.33880076 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.01611847 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.06876971 19.09354210 + vae.encoder_f1 0.06877109 19.09370041 + vae.decoder 0.00023999 0.06050764 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 10.16853457 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29312 +BPFP 0.1037 bits/point +EBPFP 0.2074 equivalent bits/point +MSE 10.168535 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 10.1685 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,144B, BPFP=0.1548 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,748B, BPFP=0.1419 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,696B, BPFP=0.1445 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,208B, BPFP=0.0490 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,208B, BPFP=0.0490 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,728B, BPFP=0.0527 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 5.78112094 + text_encoder-item0.clip_prompt_embeds 0.00025253 24.22607210 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 6.92814941 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.34524695 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.01492829 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00595097 4.11427593 + vae.encoder_f1 0.00595882 4.11472607 + vae.decoder 0.00020134 0.14641362 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 3.23188544 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27356 +BPFP 0.0968 bits/point +EBPFP 0.1936 equivalent bits/point +MSE 3.231885 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2319 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,156B, BPFP=0.1564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,788B, BPFP=0.1451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,700B, BPFP=0.1446 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,512B, BPFP=0.0536 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,516B, BPFP=0.0536 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,912B, BPFP=0.0583 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 5.72955195 + text_encoder-item0.clip_prompt_embeds 0.00022201 24.24714852 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 6.76963196 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.38071146 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.01541106 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00831743 8.80690289 + vae.encoder_f1 0.00831926 8.80749607 + vae.decoder 0.00028593 0.15186977 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 5.41089926 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28208 +BPFP 0.0998 bits/point +EBPFP 0.1996 equivalent bits/point +MSE 5.410899 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.4109 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,152B, BPFP=0.1558 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,784B, BPFP=0.1448 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,824B, BPFP=0.1477 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,824B, BPFP=0.0583 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,824B, BPFP=0.0583 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,860B, BPFP=0.0568 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 5.75860278 + text_encoder-item0.clip_prompt_embeds 0.00026808 24.30955763 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 6.86054687 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.35024220 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.01687938 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00606586 13.70860386 + vae.encoder_f1 0.00607066 13.70866013 + vae.decoder 0.00019664 0.11814478 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 7.68068749 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28888 +BPFP 0.1022 bits/point +EBPFP 0.2044 equivalent bits/point +MSE 7.680687 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.6807 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,128B, BPFP=0.1526 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,788B, BPFP=0.1451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,720B, BPFP=0.1451 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,640B, BPFP=0.0555 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,640B, BPFP=0.0555 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,632B, BPFP=0.0498 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 5.78742154 + text_encoder-item0.clip_prompt_embeds 0.00023198 24.26179696 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 6.83188248 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.37969194 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.01576998 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.05216765 17.13986969 + vae.encoder_f1 0.05216896 17.13992310 + vae.decoder 0.00017960 0.08757359 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 9.26832742 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28176 +BPFP 0.0997 bits/point +EBPFP 0.1994 equivalent bits/point +MSE 9.268327 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.2683 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,164B, BPFP=0.1575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,764B, BPFP=0.1432 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,732B, BPFP=0.1454 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,988B, BPFP=0.0609 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,988B, BPFP=0.0609 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,628B, BPFP=0.0497 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 5.75503794 + text_encoder-item0.clip_prompt_embeds 0.00023125 24.26933256 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 6.81072845 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.33328041 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.01485953 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00620361 10.95679474 + vae.encoder_f1 0.00620966 10.95666218 + vae.decoder 0.00020748 0.09139463 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 6.39923827 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28888 +BPFP 0.1022 bits/point +EBPFP 0.2044 equivalent bits/point +MSE 6.399238 +---------------------- -------------------------------------------------------- +Time: 0.765s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3992 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,156B, BPFP=0.1564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,796B, BPFP=0.1458 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,764B, BPFP=0.1462 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,808B, BPFP=0.0581 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,808B, BPFP=0.0581 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,804B, BPFP=0.0551 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 5.69183668 + text_encoder-item0.clip_prompt_embeds 0.00023066 24.26931565 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 6.73937454 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.38397677 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.01519485 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.03159856 12.68405533 + vae.encoder_f1 0.03160188 12.69400120 + vae.decoder 0.00018417 0.11957284 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 7.20808512 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28760 +BPFP 0.1018 bits/point +EBPFP 0.2035 equivalent bits/point +MSE 7.208085 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.2081 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,180B, BPFP=0.1596 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,784B, BPFP=0.1448 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,812B, BPFP=0.1474 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,168B, BPFP=0.0636 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,168B, BPFP=0.0636 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,768B, BPFP=0.0540 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 5.74063810 + text_encoder-item0.clip_prompt_embeds 0.00024948 24.26119242 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 6.68227081 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.34563729 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.01701609 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.03490865 24.90828323 + vae.encoder_f1 0.03491008 24.90911674 + vae.decoder 0.00028462 0.10069860 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 12.87134550 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29504 +BPFP 0.1044 bits/point +EBPFP 0.2088 equivalent bits/point +MSE 12.871346 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 12.8713 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,168B, BPFP=0.1580 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,812B, BPFP=0.1471 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,648B, BPFP=0.1433 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,860B, BPFP=0.0436 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,860B, BPFP=0.0436 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,556B, BPFP=0.0475 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 5.65324847 + text_encoder-item0.clip_prompt_embeds 0.00021560 24.21580129 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 6.63041153 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.37339648 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.01505126 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00544735 2.35363317 + vae.encoder_f1 0.00544843 2.35362124 + vae.decoder 0.00018632 0.12483864 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 2.41351048 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26532 +BPFP 0.0939 bits/point +EBPFP 0.1878 equivalent bits/point +MSE 2.413510 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4135 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,132B, BPFP=0.1531 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,784B, BPFP=0.1448 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,748B, BPFP=0.1458 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,000B, BPFP=0.0610 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,000B, BPFP=0.0610 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,836B, BPFP=0.0560 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 5.72521845 + text_encoder-item0.clip_prompt_embeds 0.00022698 24.26852721 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 6.78311615 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.35798340 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.01503220 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00630479 10.46801281 + vae.encoder_f1 0.00631430 10.46839523 + vae.decoder 0.00018596 0.11696267 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 6.17669472 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29124 +BPFP 0.1030 bits/point +EBPFP 0.2061 equivalent bits/point +MSE 6.176695 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.1767 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,184B, BPFP=0.1602 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,808B, BPFP=0.1468 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,724B, BPFP=0.1452 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,824B, BPFP=0.0583 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,824B, BPFP=0.0583 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,816B, BPFP=0.0554 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 5.73666700 + text_encoder-item0.clip_prompt_embeds 0.00024643 24.29924877 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 6.80007629 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.35418329 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.01471705 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00612578 8.21601295 + vae.encoder_f1 0.00613243 8.21632767 + vae.decoder 0.00018179 0.12719609 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 5.13406717 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28804 +BPFP 0.1019 bits/point +EBPFP 0.2038 equivalent bits/point +MSE 5.134067 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.1341 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,084B, BPFP=0.1466 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,740B, BPFP=0.1412 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,608B, BPFP=0.1422 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,668B, BPFP=0.0407 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,668B, BPFP=0.0407 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,884B, BPFP=0.0575 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.441s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 5.82414818 + text_encoder-item0.clip_prompt_embeds 0.00024049 24.21191195 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 6.68019333 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.35836927 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.01514624 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00526071 1.04780591 + vae.encoder_f1 0.00526072 1.04778230 + vae.decoder 0.00016981 0.14380035 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 1.80944788 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26280 +BPFP 0.0930 bits/point +EBPFP 0.1860 equivalent bits/point +MSE 1.809448 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.441s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8094 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,112B, BPFP=0.1504 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,768B, BPFP=0.1435 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,696B, BPFP=0.1445 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,784B, BPFP=0.0577 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,784B, BPFP=0.0577 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,984B, BPFP=0.0605 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 5.72178968 + text_encoder-item0.clip_prompt_embeds 0.00022843 24.22470872 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 6.71427841 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.29777333 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.01461234 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00622977 11.32322216 + vae.encoder_f1 0.00623684 11.32327271 + vae.decoder 0.00019755 0.13922215 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 6.57194799 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28748 +BPFP 0.1017 bits/point +EBPFP 0.2034 equivalent bits/point +MSE 6.571948 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.5719 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,164B, BPFP=0.1575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,780B, BPFP=0.1445 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,740B, BPFP=0.1456 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,624B, BPFP=0.0553 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,624B, BPFP=0.0553 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,940B, BPFP=0.0592 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 5.73328781 + text_encoder-item0.clip_prompt_embeds 0.00026004 24.26249450 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 6.67646713 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.33587329 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.01517334 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00725303 7.38592720 + vae.encoder_f1 0.00725507 7.38597155 + vae.decoder 0.00017991 0.08366979 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 4.74222370 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28496 +BPFP 0.1008 bits/point +EBPFP 0.2017 equivalent bits/point +MSE 4.742224 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7422 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,212B, BPFP=0.1640 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,800B, BPFP=0.1461 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,748B, BPFP=0.1458 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,484B, BPFP=0.0532 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,484B, BPFP=0.0532 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,624B, BPFP=0.0496 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 5.74296570 + text_encoder-item0.clip_prompt_embeds 0.00031748 24.31898928 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 6.74607086 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.36045516 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.01647384 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.42111695 23.41371346 + vae.encoder_f1 0.42111716 23.41392326 + vae.decoder 0.00019827 0.07288475 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 12.17696114 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27980 +BPFP 0.0990 bits/point +EBPFP 0.1980 equivalent bits/point +MSE 12.176961 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 12.1770 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,160B, BPFP=0.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,728B, BPFP=0.1403 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,780B, BPFP=0.1466 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,776B, BPFP=0.0576 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,776B, BPFP=0.0576 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,864B, BPFP=0.0569 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 5.74460602 + text_encoder-item0.clip_prompt_embeds 0.00024951 24.26144185 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 6.81055756 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.34078302 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.01644513 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.10376993 24.86732483 + vae.encoder_f1 0.10377157 24.86749649 + vae.decoder 0.00019787 0.08455858 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 12.85011477 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28712 +BPFP 0.1016 bits/point +EBPFP 0.2032 equivalent bits/point +MSE 12.850115 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 12.8501 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,152B, BPFP=0.1558 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,772B, BPFP=0.1438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,664B, BPFP=0.1437 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,976B, BPFP=0.0607 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,976B, BPFP=0.0607 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,672B, BPFP=0.0510 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 5.87996674 + text_encoder-item0.clip_prompt_embeds 0.00022350 24.28017832 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 6.72210541 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.36878825 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.01481057 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.01346414 15.64173317 + vae.encoder_f1 0.01346933 15.64241791 + vae.decoder 0.00019243 0.07242684 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 8.57177064 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28836 +BPFP 0.1020 bits/point +EBPFP 0.2041 equivalent bits/point +MSE 8.571771 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.5718 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,180B, BPFP=0.1596 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,784B, BPFP=0.1448 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,856B, BPFP=0.1485 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,464B, BPFP=0.0529 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,464B, BPFP=0.0529 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,728B, BPFP=0.0527 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.436s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 5.73069572 + text_encoder-item0.clip_prompt_embeds 0.00024958 24.29659810 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 6.80808182 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.33680166 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.01790110 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.11196710 21.94625473 + vae.encoder_f1 0.11196851 21.94635582 + vae.decoder 0.00023459 0.08382343 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 11.49625692 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28104 +BPFP 0.0994 bits/point +EBPFP 0.1989 equivalent bits/point +MSE 11.496257 +---------------------- -------------------------------------------------------- +Time: 0.735s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.436s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.4963 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,180B, BPFP=0.1596 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,800B, BPFP=0.1461 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,700B, BPFP=0.1446 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,892B, BPFP=0.0594 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,892B, BPFP=0.0594 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,592B, BPFP=0.0486 +⌛️ [2/4] FRONTEND: Frontend time: 0.306s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 5.87242317 + text_encoder-item0.clip_prompt_embeds 0.00025929 24.32754371 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 6.69079285 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.38777708 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.01540705 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00675017 13.16869354 + vae.encoder_f1 0.00675421 13.16809654 + vae.decoder 0.00023635 0.09147765 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 7.42889479 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28684 +BPFP 0.1015 bits/point +EBPFP 0.2030 equivalent bits/point +MSE 7.428895 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.009s, Pack+Encode: 0.306s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.4289 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,164B, BPFP=0.1575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,796B, BPFP=0.1458 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,816B, BPFP=0.1475 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,132B, BPFP=0.0630 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,132B, BPFP=0.0630 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,796B, BPFP=0.0548 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 5.68802071 + text_encoder-item0.clip_prompt_embeds 0.00064775 24.32411729 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 6.71104431 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.34825828 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.01647133 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00728993 17.95944786 + vae.encoder_f1 0.00729572 17.95981979 + vae.decoder 0.00026488 0.09310858 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 9.64939265 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29464 +BPFP 0.1043 bits/point +EBPFP 0.2085 equivalent bits/point +MSE 9.649393 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.6494 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,192B, BPFP=0.1613 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,788B, BPFP=0.1451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,880B, BPFP=0.1491 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,416B, BPFP=0.0521 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,416B, BPFP=0.0521 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,584B, BPFP=0.0483 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.441s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 5.85151164 + text_encoder-item0.clip_prompt_embeds 0.00023188 24.28694661 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 6.73026810 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.34666245 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.01700814 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00613207 9.89985275 + vae.encoder_f1 0.00613899 9.89990044 + vae.decoder 0.00023812 0.09317003 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 5.91064089 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27904 +BPFP 0.0987 bits/point +EBPFP 0.1975 equivalent bits/point +MSE 5.910641 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.008s, Pack+Encode: 0.287s, Decode+Unpack: 0.441s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.9106 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,168B, BPFP=0.1580 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,792B, BPFP=0.1455 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,764B, BPFP=0.1462 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,832B, BPFP=0.0585 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,832B, BPFP=0.0585 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,816B, BPFP=0.0554 +⌛️ [2/4] FRONTEND: Frontend time: 0.285s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.437s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 5.81733513 + text_encoder-item0.clip_prompt_embeds 0.00023678 24.26514729 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 6.85713120 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.36630816 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.01577506 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00636537 12.98287964 + vae.encoder_f1 0.00636991 12.98288441 + vae.decoder 0.00025538 0.10599542 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 7.34210194 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28832 +BPFP 0.1020 bits/point +EBPFP 0.2040 equivalent bits/point +MSE 7.342102 +---------------------- -------------------------------------------------------- +Time: 0.731s Load: 0.008s, Pack+Encode: 0.285s, Decode+Unpack: 0.437s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.3421 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,220B, BPFP=0.1650 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,792B, BPFP=0.1455 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,820B, BPFP=0.1476 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,012B, BPFP=0.0612 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,012B, BPFP=0.0612 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,664B, BPFP=0.0508 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.434s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 5.77515157 + text_encoder-item0.clip_prompt_embeds 0.00023432 24.29077677 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 6.47199173 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.30474249 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.01530312 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.23155926 26.19971657 + vae.encoder_f1 0.23156048 26.20021248 + vae.decoder 0.00018572 0.06752595 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 13.46499156 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29144 +BPFP 0.1031 bits/point +EBPFP 0.2062 equivalent bits/point +MSE 13.464992 +---------------------- -------------------------------------------------------- +Time: 0.731s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.434s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 13.4650 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,148B, BPFP=0.1553 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,760B, BPFP=0.1429 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,724B, BPFP=0.1452 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,752B, BPFP=0.0573 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,752B, BPFP=0.0573 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,788B, BPFP=0.0546 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 5.80679766 + text_encoder-item0.clip_prompt_embeds 0.00022528 24.24600286 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 6.82210236 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.33051167 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.01492953 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00729824 14.75702477 + vae.encoder_f1 0.00730369 14.75880337 + vae.decoder 0.00019938 0.11811209 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 8.16450754 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28548 +BPFP 0.1010 bits/point +EBPFP 0.2020 equivalent bits/point +MSE 8.164508 +---------------------- -------------------------------------------------------- +Time: 0.733s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.1645 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,184B, BPFP=0.1602 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,820B, BPFP=0.1477 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,788B, BPFP=0.1468 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,408B, BPFP=0.0520 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,408B, BPFP=0.0520 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,900B, BPFP=0.0580 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.434s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 5.78088188 + text_encoder-item0.clip_prompt_embeds 0.00022149 24.25458900 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 6.75625076 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.35984184 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.01569703 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00564371 5.51919794 + vae.encoder_f1 0.00565042 5.51930666 + vae.decoder 0.00019980 0.13523769 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 3.88346045 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28136 +BPFP 0.0996 bits/point +EBPFP 0.1991 equivalent bits/point +MSE 3.883460 +---------------------- -------------------------------------------------------- +Time: 0.729s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.434s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8835 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,152B, BPFP=0.1558 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,772B, BPFP=0.1438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,696B, BPFP=0.1445 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,520B, BPFP=0.0537 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,516B, BPFP=0.0536 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,892B, BPFP=0.0577 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.436s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 5.78026899 + text_encoder-item0.clip_prompt_embeds 0.00022173 24.26978068 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 6.88730164 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.37998348 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.01520313 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00576096 5.16675186 + vae.encoder_f1 0.00576981 5.16753197 + vae.decoder 0.00019592 0.14493959 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 3.72256816 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28172 +BPFP 0.0997 bits/point +EBPFP 0.1994 equivalent bits/point +MSE 3.722568 +---------------------- -------------------------------------------------------- +Time: 0.732s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.436s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7226 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,172B, BPFP=0.1585 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,748B, BPFP=0.1419 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,776B, BPFP=0.1465 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,096B, BPFP=0.0472 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,096B, BPFP=0.0472 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,712B, BPFP=0.0522 +⌛️ [2/4] FRONTEND: Frontend time: 0.285s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.438s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 5.71669261 + text_encoder-item0.clip_prompt_embeds 0.00025917 24.29297298 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 6.62438354 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.36993805 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.01652527 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00594818 5.85003233 + vae.encoder_f1 0.00595328 5.85095024 + vae.decoder 0.00023462 0.09212957 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 4.03354360 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27228 +BPFP 0.0963 bits/point +EBPFP 0.1927 equivalent bits/point +MSE 4.033544 +---------------------- -------------------------------------------------------- +Time: 0.731s Load: 0.007s, Pack+Encode: 0.285s, Decode+Unpack: 0.438s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0335 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,112B, BPFP=0.1504 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,720B, BPFP=0.1396 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,660B, BPFP=0.1436 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,100B, BPFP=0.0626 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,100B, BPFP=0.0626 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,736B, BPFP=0.0530 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 5.73236148 + text_encoder-item0.clip_prompt_embeds 0.00022579 24.25447485 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 6.57181778 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.37098753 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.01577226 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.85445058 26.68061447 + vae.encoder_f1 0.85445166 26.67980957 + vae.decoder 0.00025257 0.05969526 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 13.68885280 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29052 +BPFP 0.1028 bits/point +EBPFP 0.2056 equivalent bits/point +MSE 13.688853 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 13.6889 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,172B, BPFP=0.1585 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,792B, BPFP=0.1455 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,768B, BPFP=0.1463 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,212B, BPFP=0.0643 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,212B, BPFP=0.0643 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,128B, BPFP=0.0649 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 5.75003688 + text_encoder-item0.clip_prompt_embeds 0.00025458 24.24223400 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 6.77159958 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.36546231 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.01605413 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00628510 11.42083454 + vae.encoder_f1 0.00629234 11.42085838 + vae.decoder 0.00023521 0.14467923 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 6.62149621 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29904 +BPFP 0.1058 bits/point +EBPFP 0.2116 equivalent bits/point +MSE 6.621496 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.6215 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,156B, BPFP=0.1564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,788B, BPFP=0.1451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,812B, BPFP=0.1474 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,352B, BPFP=0.0511 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,352B, BPFP=0.0511 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,608B, BPFP=0.0491 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 5.77724648 + text_encoder-item0.clip_prompt_embeds 0.00022807 24.29415458 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 6.84835892 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.31266852 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.01668615 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00573429 6.13979244 + vae.encoder_f1 0.00574192 6.14012480 + vae.decoder 0.00017875 0.11092283 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 4.16767250 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27692 +BPFP 0.0980 bits/point +EBPFP 0.1960 equivalent bits/point +MSE 4.167672 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1677 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,092B, BPFP=0.1477 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,716B, BPFP=0.1393 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,400B, BPFP=0.1370 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,592B, BPFP=0.0548 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,592B, BPFP=0.0548 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,992B, BPFP=0.0608 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 5.80988757 + text_encoder-item0.clip_prompt_embeds 0.00027120 24.24828150 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 7.08968430 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.41396191 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.01506292 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00781570 14.65964699 + vae.encoder_f1 0.00781878 14.66036797 + vae.decoder 0.00029724 0.13765794 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 8.12523623 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28012 +BPFP 0.0991 bits/point +EBPFP 0.1982 equivalent bits/point +MSE 8.125236 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.1252 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,156B, BPFP=0.1564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,776B, BPFP=0.1442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,712B, BPFP=0.1449 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,880B, BPFP=0.0592 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,880B, BPFP=0.0592 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,104B, BPFP=0.0642 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.441s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 5.70816612 + text_encoder-item0.clip_prompt_embeds 0.00022930 24.24225725 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 6.70215759 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.37149892 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.01536084 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00577752 6.49729443 + vae.encoder_f1 0.00578475 6.49740505 + vae.decoder 0.00024190 0.16649771 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 4.34077860 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29132 +BPFP 0.1031 bits/point +EBPFP 0.2062 equivalent bits/point +MSE 4.340779 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.441s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.3408 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,184B, BPFP=0.1602 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,764B, BPFP=0.1432 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,824B, BPFP=0.1477 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,056B, BPFP=0.0619 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,060B, BPFP=0.0620 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,444B, BPFP=0.0441 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.435s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 5.79866600 + text_encoder-item0.clip_prompt_embeds 0.00028764 24.32251082 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 6.77136383 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.35451735 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.01651472 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.03343784 22.07976723 + vae.encoder_f1 0.03344063 22.07995224 + vae.decoder 0.00016139 0.05212675 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 11.55577915 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28956 +BPFP 0.1025 bits/point +EBPFP 0.2049 equivalent bits/point +MSE 11.555779 +---------------------- -------------------------------------------------------- +Time: 0.730s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.435s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.5558 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,128B, BPFP=0.1526 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,768B, BPFP=0.1435 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,756B, BPFP=0.1460 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,068B, BPFP=0.0621 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,068B, BPFP=0.0621 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,716B, BPFP=0.0524 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.438s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 5.84542274 + text_encoder-item0.clip_prompt_embeds 0.00023094 24.27157738 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 6.78455353 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.31926482 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.01503733 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00637455 14.40603065 + vae.encoder_f1 0.00637988 14.40610409 + vae.decoder 0.00020059 0.12211464 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 8.00198186 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29132 +BPFP 0.1031 bits/point +EBPFP 0.2062 equivalent bits/point +MSE 8.001982 +---------------------- -------------------------------------------------------- +Time: 0.733s Load: 0.008s, Pack+Encode: 0.287s, Decode+Unpack: 0.438s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.0020 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,160B, BPFP=0.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,788B, BPFP=0.1451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,816B, BPFP=0.1475 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,716B, BPFP=0.0567 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,716B, BPFP=0.0567 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,856B, BPFP=0.0566 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 5.87130928 + text_encoder-item0.clip_prompt_embeds 0.00025217 24.26593361 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 6.70983353 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.33872883 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.01645689 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00581597 6.16839981 + vae.encoder_f1 0.00582356 6.16843033 + vae.decoder 0.00019494 0.12166782 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 4.18243493 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28676 +BPFP 0.1015 bits/point +EBPFP 0.2029 equivalent bits/point +MSE 4.182435 +---------------------- -------------------------------------------------------- +Time: 0.734s Load: 0.008s, Pack+Encode: 0.287s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1824 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,164B, BPFP=0.1575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,764B, BPFP=0.1432 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,768B, BPFP=0.1463 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,212B, BPFP=0.0490 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,212B, BPFP=0.0490 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,628B, BPFP=0.0497 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 5.82093112 + text_encoder-item0.clip_prompt_embeds 0.00026975 24.27391098 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 6.73860779 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.31804750 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.01470798 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 1.11695218 24.34625244 + vae.encoder_f1 1.11695278 24.34636497 + vae.decoder 0.00019720 0.08130170 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 12.60714462 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27376 +BPFP 0.0969 bits/point +EBPFP 0.1937 equivalent bits/point +MSE 12.607145 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 12.6071 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,172B, BPFP=0.1585 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,792B, BPFP=0.1455 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,740B, BPFP=0.1456 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,976B, BPFP=0.0607 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,976B, BPFP=0.0607 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,856B, BPFP=0.0566 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 5.64992714 + text_encoder-item0.clip_prompt_embeds 0.00025545 24.26643669 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 6.54117508 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.32422886 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.01579882 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.01535016 18.54830360 + vae.encoder_f1 0.01535382 18.54838371 + vae.decoder 0.00021460 0.12156070 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 9.92295727 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29140 +BPFP 0.1031 bits/point +EBPFP 0.2062 equivalent bits/point +MSE 9.922957 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.9230 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,152B, BPFP=0.1558 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,780B, BPFP=0.1445 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,728B, BPFP=0.1453 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,360B, BPFP=0.0513 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,360B, BPFP=0.0513 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,708B, BPFP=0.0521 +⌛️ [2/4] FRONTEND: Frontend time: 0.305s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 5.81946945 + text_encoder-item0.clip_prompt_embeds 0.00022628 24.25734536 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 6.74286423 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.36743612 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.01612080 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00589589 4.35851908 + vae.encoder_f1 0.00590398 4.35809040 + vae.decoder 0.00017838 0.14987202 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 3.34721450 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27712 +BPFP 0.0981 bits/point +EBPFP 0.1961 equivalent bits/point +MSE 3.347214 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.009s, Pack+Encode: 0.305s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3472 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,184B, BPFP=0.1602 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,800B, BPFP=0.1461 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,744B, BPFP=0.1457 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,992B, BPFP=0.0609 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,992B, BPFP=0.0609 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,644B, BPFP=0.0502 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 5.80686633 + text_encoder-item0.clip_prompt_embeds 0.00031548 24.24825191 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 6.64561234 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.36139086 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.01501870 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00725484 16.72661209 + vae.encoder_f1 0.00725992 16.72674561 + vae.decoder 0.00019960 0.07568641 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 9.07395639 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28976 +BPFP 0.1025 bits/point +EBPFP 0.2050 equivalent bits/point +MSE 9.073956 +---------------------- -------------------------------------------------------- +Time: 0.739s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.0740 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,160B, BPFP=0.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,824B, BPFP=0.1481 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,588B, BPFP=0.1417 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,088B, BPFP=0.0624 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,088B, BPFP=0.0624 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,868B, BPFP=0.0570 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 5.75429153 + text_encoder-item0.clip_prompt_embeds 0.00021831 24.28387108 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 7.15217133 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.32867119 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.01357073 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00923516 15.17395973 + vae.encoder_f1 0.00923823 15.17401695 + vae.decoder 0.00019521 0.08334219 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 8.35432792 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29240 +BPFP 0.1035 bits/point +EBPFP 0.2069 equivalent bits/point +MSE 8.354328 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.3543 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,152B, BPFP=0.1558 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,764B, BPFP=0.1432 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,708B, BPFP=0.1448 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,992B, BPFP=0.0609 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,992B, BPFP=0.0609 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,904B, BPFP=0.0581 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 5.85043271 + text_encoder-item0.clip_prompt_embeds 0.00062166 24.28001344 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 6.64838943 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.36209352 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.01592715 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00831779 16.15068817 + vae.encoder_f1 0.00832197 16.15093613 + vae.decoder 0.00023271 0.11198179 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 8.81210038 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29136 +BPFP 0.1031 bits/point +EBPFP 0.2062 equivalent bits/point +MSE 8.812100 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.8121 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,180B, BPFP=0.1596 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,780B, BPFP=0.1445 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,880B, BPFP=0.1491 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,536B, BPFP=0.0540 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,536B, BPFP=0.0540 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,440B, BPFP=0.0439 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.438s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 5.76558431 + text_encoder-item0.clip_prompt_embeds 0.00022938 24.29537634 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 6.64114380 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.32524323 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.01625714 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00626977 8.98421288 + vae.encoder_f1 0.00627489 8.98418522 + vae.decoder 0.00017842 0.08132544 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 5.48370793 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27980 +BPFP 0.0990 bits/point +EBPFP 0.1980 equivalent bits/point +MSE 5.483708 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.438s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.4837 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,116B, BPFP=0.1510 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,772B, BPFP=0.1438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,644B, BPFP=0.1432 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,184B, BPFP=0.0486 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,184B, BPFP=0.0486 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,640B, BPFP=0.0500 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 5.74997711 + text_encoder-item0.clip_prompt_embeds 0.00022180 24.24585912 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 6.75995255 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.35571226 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.01471722 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00585720 5.57980251 + vae.encoder_f1 0.00586586 5.57981777 + vae.decoder 0.00016520 0.12324892 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 3.90960182 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27168 +BPFP 0.0961 bits/point +EBPFP 0.1923 equivalent bits/point +MSE 3.909602 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9096 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,136B, BPFP=0.1537 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,772B, BPFP=0.1438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,704B, BPFP=0.1447 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,172B, BPFP=0.0484 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,172B, BPFP=0.0484 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,396B, BPFP=0.0426 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 5.66640218 + text_encoder-item0.clip_prompt_embeds 0.00025784 24.20995882 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 6.58122711 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.33665835 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.01495015 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00734802 13.59069920 + vae.encoder_f1 0.00734987 13.59074402 + vae.decoder 0.00018093 0.09359342 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 7.61950218 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26980 +BPFP 0.0955 bits/point +EBPFP 0.1909 equivalent bits/point +MSE 7.619502 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.6195 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,172B, BPFP=0.1585 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,768B, BPFP=0.1435 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,632B, BPFP=0.1429 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,868B, BPFP=0.0590 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,868B, BPFP=0.0590 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,668B, BPFP=0.0509 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 5.78863589 + text_encoder-item0.clip_prompt_embeds 0.00023510 24.31483149 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 6.83478394 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.34772426 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.01354429 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00637359 14.98333645 + vae.encoder_f1 0.00637830 14.98235512 + vae.decoder 0.00018566 0.10450020 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 8.26960382 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28604 +BPFP 0.1012 bits/point +EBPFP 0.2024 equivalent bits/point +MSE 8.269604 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.2696 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,156B, BPFP=0.1564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,820B, BPFP=0.1477 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,732B, BPFP=0.1454 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,932B, BPFP=0.0600 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,932B, BPFP=0.0600 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,428B, BPFP=0.0436 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 5.81313070 + text_encoder-item0.clip_prompt_embeds 0.00026418 24.31023192 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 6.60910416 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.37989573 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.01548874 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.01530954 16.77688408 + vae.encoder_f1 0.01531230 16.77735138 + vae.decoder 0.00017892 0.05725776 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 9.09768645 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28628 +BPFP 0.1013 bits/point +EBPFP 0.2026 equivalent bits/point +MSE 9.097686 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.0977 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,160B, BPFP=0.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,740B, BPFP=0.1412 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,800B, BPFP=0.1471 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,308B, BPFP=0.0505 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,308B, BPFP=0.0505 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,804B, BPFP=0.0551 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 5.77971967 + text_encoder-item0.clip_prompt_embeds 0.00021481 24.23361616 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 6.49630737 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.35061689 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.01715753 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00591154 11.38177490 + vae.encoder_f1 0.00591973 11.38178825 + vae.decoder 0.00025286 0.12680075 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 6.60044191 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27748 +BPFP 0.0982 bits/point +EBPFP 0.1964 equivalent bits/point +MSE 6.600442 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.6004 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,156B, BPFP=0.1564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,772B, BPFP=0.1438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,792B, BPFP=0.1469 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,488B, BPFP=0.0532 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,488B, BPFP=0.0532 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,880B, BPFP=0.0574 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.441s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 5.75969823 + text_encoder-item0.clip_prompt_embeds 0.00023458 24.28240835 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 6.78627014 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.38601011 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.01725383 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00588703 4.39165306 + vae.encoder_f1 0.00589573 4.39207792 + vae.decoder 0.00053402 0.13353342 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 3.36251208 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28196 +BPFP 0.0998 bits/point +EBPFP 0.1995 equivalent bits/point +MSE 3.362512 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.441s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3625 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,148B, BPFP=0.1553 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,744B, BPFP=0.1416 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,652B, BPFP=0.1434 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,864B, BPFP=0.0590 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,864B, BPFP=0.0590 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,616B, BPFP=0.0493 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 5.71060626 + text_encoder-item0.clip_prompt_embeds 0.00022882 24.26823551 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 6.78188248 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.34474016 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.01482976 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00659691 14.26236439 + vae.encoder_f1 0.00660300 14.26275730 + vae.decoder 0.00023739 0.08458105 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 7.93202322 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28516 +BPFP 0.1009 bits/point +EBPFP 0.2018 equivalent bits/point +MSE 7.932023 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.9320 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,184B, BPFP=0.1602 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,776B, BPFP=0.1442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,796B, BPFP=0.1470 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,876B, BPFP=0.0439 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,872B, BPFP=0.0438 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,736B, BPFP=0.0530 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 5.75260989 + text_encoder-item0.clip_prompt_embeds 0.00023928 24.22988324 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 6.78766098 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.35576307 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.01568209 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00583864 3.21823239 + vae.encoder_f1 0.00583800 3.21834946 + vae.decoder 0.00018889 0.13316363 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 2.81528957 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26868 +BPFP 0.0951 bits/point +EBPFP 0.1901 equivalent bits/point +MSE 2.815290 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8153 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,180B, BPFP=0.1596 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,784B, BPFP=0.1448 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,852B, BPFP=0.1484 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,984B, BPFP=0.0455 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,984B, BPFP=0.0455 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,740B, BPFP=0.0531 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 5.75571696 + text_encoder-item0.clip_prompt_embeds 0.00024821 24.23195473 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 6.58498306 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.36190503 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.01849538 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00570467 2.72851539 + vae.encoder_f1 0.00570488 2.72841358 + vae.decoder 0.00017302 0.09849656 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 2.58470498 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27144 +BPFP 0.0960 bits/point +EBPFP 0.1921 equivalent bits/point +MSE 2.584705 +---------------------- -------------------------------------------------------- +Time: 0.739s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5847 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,116B, BPFP=0.1510 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,776B, BPFP=0.1442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,760B, BPFP=0.1461 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,124B, BPFP=0.0477 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,124B, BPFP=0.0477 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,440B, BPFP=0.0439 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 5.73679860 + text_encoder-item0.clip_prompt_embeds 0.00021458 24.27995637 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 6.51713715 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.36269607 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.01575648 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00914783 11.49305725 + vae.encoder_f1 0.00914958 11.49321461 + vae.decoder 0.00017527 0.06352277 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 6.64628828 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26964 +BPFP 0.0954 bits/point +EBPFP 0.1908 equivalent bits/point +MSE 6.646288 +---------------------- -------------------------------------------------------- +Time: 0.736s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.6463 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,144B, BPFP=0.1548 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,752B, BPFP=0.1422 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,736B, BPFP=0.1455 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,232B, BPFP=0.0493 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,232B, BPFP=0.0493 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,812B, BPFP=0.0553 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 5.77975972 + text_encoder-item0.clip_prompt_embeds 0.00022150 24.28102594 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 6.76877213 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.37127896 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.01513441 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00578482 3.96864533 + vae.encoder_f1 0.00579739 3.96859503 + vae.decoder 0.00017668 0.14751709 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 3.16686865 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27532 +BPFP 0.0974 bits/point +EBPFP 0.1948 equivalent bits/point +MSE 3.166869 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1669 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,140B, BPFP=0.1542 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,736B, BPFP=0.1409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,716B, BPFP=0.1450 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,604B, BPFP=0.0550 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,604B, BPFP=0.0550 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,736B, BPFP=0.0530 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 5.66402817 + text_encoder-item0.clip_prompt_embeds 0.00023894 24.24953708 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 6.73278809 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.33407510 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.01494290 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00958025 13.18850231 + vae.encoder_f1 0.00958229 13.18662453 + vae.decoder 0.00019995 0.10729281 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 7.43512515 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28160 +BPFP 0.0996 bits/point +EBPFP 0.1993 equivalent bits/point +MSE 7.435125 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.4351 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,136B, BPFP=0.1537 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,736B, BPFP=0.1409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,736B, BPFP=0.1455 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,776B, BPFP=0.0424 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,772B, BPFP=0.0423 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,696B, BPFP=0.0518 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 5.64116287 + text_encoder-item0.clip_prompt_embeds 0.00023387 24.28664012 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 6.89580231 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.36432915 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.01678804 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00567713 2.37981486 + vae.encoder_f1 0.00567905 2.37979341 + vae.decoder 0.00019376 0.15051791 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 2.43047374 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26480 +BPFP 0.0937 bits/point +EBPFP 0.1874 equivalent bits/point +MSE 2.430474 +---------------------- -------------------------------------------------------- +Time: 0.736s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4305 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,172B, BPFP=0.1585 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,812B, BPFP=0.1471 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,412B, BPFP=0.0521 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,412B, BPFP=0.0521 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,692B, BPFP=0.0516 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 5.70177078 + text_encoder-item0.clip_prompt_embeds 0.00024281 24.25710650 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 6.63329849 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.34845732 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.01796292 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.02387581 12.99249172 + vae.encoder_f1 0.02387858 12.99257088 + vae.decoder 0.00018648 0.09932342 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 7.34495417 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27980 +BPFP 0.0990 bits/point +EBPFP 0.1980 equivalent bits/point +MSE 7.344954 +---------------------- -------------------------------------------------------- +Time: 0.736s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.3450 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,148B, BPFP=0.1553 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,796B, BPFP=0.1458 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,732B, BPFP=0.1454 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,208B, BPFP=0.0642 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,208B, BPFP=0.0642 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,820B, BPFP=0.0555 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.438s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 5.77770106 + text_encoder-item0.clip_prompt_embeds 0.00022399 24.24869158 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 6.63519287 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.33950421 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.01505389 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.01169517 20.80148697 + vae.encoder_f1 0.01169969 20.80154991 + vae.decoder 0.00021186 0.07015707 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 10.96214252 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29540 +BPFP 0.1045 bits/point +EBPFP 0.2090 equivalent bits/point +MSE 10.962143 +---------------------- -------------------------------------------------------- +Time: 0.732s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.438s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 10.9621 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,148B, BPFP=0.1553 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,796B, BPFP=0.1458 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,776B, BPFP=0.1465 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,620B, BPFP=0.0552 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,620B, BPFP=0.0552 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,200B, BPFP=0.0671 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.432s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 5.90689850 + text_encoder-item0.clip_prompt_embeds 0.00022123 24.29233250 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 6.63176880 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.35000428 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.01664901 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.32749966 29.60424805 + vae.encoder_f1 0.32750070 29.60444450 + vae.decoder 0.00039956 0.18160170 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 15.05939811 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28788 +BPFP 0.1019 bits/point +EBPFP 0.2037 equivalent bits/point +MSE 15.059398 +---------------------- -------------------------------------------------------- +Time: 0.727s Load: 0.008s, Pack+Encode: 0.287s, Decode+Unpack: 0.432s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 15.0594 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,152B, BPFP=0.1558 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,736B, BPFP=0.1409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,652B, BPFP=0.1434 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,104B, BPFP=0.0474 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,108B, BPFP=0.0474 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,668B, BPFP=0.0509 +⌛️ [2/4] FRONTEND: Frontend time: 0.285s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.438s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 5.65958532 + text_encoder-item0.clip_prompt_embeds 0.00024675 24.25166777 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 6.52284851 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.35902345 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.01389289 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00566967 5.49350595 + vae.encoder_f1 0.00567867 5.49348831 + vae.decoder 0.00017839 0.12892410 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 3.87024693 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27048 +BPFP 0.0957 bits/point +EBPFP 0.1914 equivalent bits/point +MSE 3.870247 +---------------------- -------------------------------------------------------- +Time: 0.731s Load: 0.008s, Pack+Encode: 0.285s, Decode+Unpack: 0.438s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8702 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,160B, BPFP=0.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,800B, BPFP=0.1461 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,696B, BPFP=0.1445 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 2,644B, BPFP=0.0403 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 2,644B, BPFP=0.0403 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,572B, BPFP=0.0480 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.433s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 5.86542511 + text_encoder-item0.clip_prompt_embeds 0.00022364 24.26723781 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 6.93710327 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.32386058 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.01517008 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00580750 2.93841028 + vae.encoder_f1 0.00580664 2.93838549 + vae.decoder 0.00018044 0.14487219 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 2.68647945 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 26140 +BPFP 0.0925 bits/point +EBPFP 0.1850 equivalent bits/point +MSE 2.686479 +---------------------- -------------------------------------------------------- +Time: 0.728s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.433s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6865 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,140B, BPFP=0.1542 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,792B, BPFP=0.1455 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,776B, BPFP=0.1465 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,808B, BPFP=0.0581 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,808B, BPFP=0.0581 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,512B, BPFP=0.0461 +⌛️ [2/4] FRONTEND: Frontend time: 0.285s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.433s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 5.70307859 + text_encoder-item0.clip_prompt_embeds 0.00030118 24.28866088 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 6.59283600 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.32249443 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.01564542 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.03869025 19.10063362 + vae.encoder_f1 0.03869358 19.10097122 + vae.decoder 0.00021614 0.07463939 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 10.17426145 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28464 +BPFP 0.1007 bits/point +EBPFP 0.2014 equivalent bits/point +MSE 10.174261 +---------------------- -------------------------------------------------------- +Time: 0.727s Load: 0.009s, Pack+Encode: 0.285s, Decode+Unpack: 0.433s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 10.1743 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,156B, BPFP=0.1564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,812B, BPFP=0.1471 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,712B, BPFP=0.0719 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,712B, BPFP=0.0719 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,572B, BPFP=0.0480 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.436s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 5.80868912 + text_encoder-item0.clip_prompt_embeds 0.00023260 24.28885958 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 6.86091232 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.35243380 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.01538620 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00839879 22.42474365 + vae.encoder_f1 0.00840224 22.42550659 + vae.decoder 0.00019463 0.07394344 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 11.71735741 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 30444 +BPFP 0.1077 bits/point +EBPFP 0.2154 equivalent bits/point +MSE 11.717357 +---------------------- -------------------------------------------------------- +Time: 0.731s Load: 0.009s, Pack+Encode: 0.286s, Decode+Unpack: 0.436s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.7174 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,148B, BPFP=0.1553 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,744B, BPFP=0.1416 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,404B, BPFP=0.1371 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,028B, BPFP=0.0615 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,028B, BPFP=0.0615 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,724B, BPFP=0.0526 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.437s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 5.70612081 + text_encoder-item0.clip_prompt_embeds 0.00023544 24.26008269 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 6.78886948 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.36115314 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.01314742 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.01160815 18.34960938 + vae.encoder_f1 0.01161249 18.34952545 + vae.decoder 0.00021720 0.08805706 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 9.82811957 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 28704 +BPFP 0.1016 bits/point +EBPFP 0.2031 equivalent bits/point +MSE 9.828120 +---------------------- -------------------------------------------------------- +Time: 0.731s Load: 0.009s, Pack+Encode: 0.286s, Decode+Unpack: 0.437s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.8281 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,160B, BPFP=0.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 480B, BPFP=3.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,760B, BPFP=0.1429 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,736B, BPFP=0.1455 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 3,416B, BPFP=0.0521 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 3,416B, BPFP=0.0521 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,864B, BPFP=0.0569 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.435s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 5.83242416 + text_encoder-item0.clip_prompt_embeds 0.00022923 24.26559118 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 6.74116058 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.32527216 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.01554413 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.02989292 14.09982967 + vae.encoder_f1 0.02989391 14.09999084 + vae.decoder 0.00034944 0.09585449 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 7.85709831 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 27976 +BPFP 0.0990 bits/point +EBPFP 0.1980 equivalent bits/point +MSE 7.857098 +---------------------- -------------------------------------------------------- +Time: 0.729s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.435s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.8571 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 292B, BPFP=3.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 1,208B, BPFP=0.1634 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 1,748B, BPFP=0.1419 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 5,800B, BPFP=0.1471 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 288B, BPFP=3.0000 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 1,196B, BPFP=0.1618 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 476B, BPFP=2.9750 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 2,036B, BPFP=0.1653 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 5,860B, BPFP=0.1486 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,024B, BPFP=0.0614 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,024B, BPFP=0.0614 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 1,676B, BPFP=0.0511 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.434s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 5.80097071 + text_encoder-item0.clip_prompt_embeds 0.00024627 24.28843682 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 6.77401352 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.34151821 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.01585483 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 3.69952774 + text_encoder-item3.clip_prompt_embeds 0.00023247 24.22424581 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 2.75604706 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.26377619 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.01569531 + vae.encoder_f0 0.00613025 9.13750267 + vae.encoder_f1 0.00613536 9.13825703 + vae.decoder 0.00018697 0.09516314 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 5.55714363 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 29104 +BPFP 0.1030 bits/point +EBPFP 0.2060 equivalent bits/point +MSE 5.557144 +---------------------- -------------------------------------------------------- +Time: 0.729s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.434s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.5571 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.001/hyperprior-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.1000 bits/point +Avg EBPFP 0.2001 equivalent bits/point +Avg MSE 7.284092 +Avg Time 0.744s +------------------------ ---------------------------- diff --git a/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log b/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..bf87bd195df0c44051f7add7992d9fcd24f5bc20 --- /dev/null +++ b/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log @@ -0,0 +1,21862 @@ +Experiment: dtufc_elic-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: sd35 + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 255 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 528B, BPFP=5.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,724B, BPFP=0.7744 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,588B, BPFP=0.7782 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,996B, BPFP=0.7101 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,540B, BPFP=0.3439 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,540B, BPFP=0.3439 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,264B, BPFP=0.2522 +⌛️ [2/4] FRONTEND: Frontend time: 3.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.652s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.89898698 + text_encoder-item0.clip_prompt_embeds 0.00025464 23.86213981 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.80910292 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.11696513 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00323178 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00635250 2.03506470 + vae.encoder_f1 0.00635834 2.03520370 + vae.decoder 0.00019940 0.05257336 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 2.20472717 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 126360 +BPFP 0.4471 bits/point +EBPFP 0.8942 equivalent bits/point +MSE 2.204727 +---------------------- -------------------------------------------------------- +Time: 4.807s Load: 0.007s, Pack+Encode: 3.148s, Decode+Unpack: 1.652s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2047 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,504B, BPFP=0.7446 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,540B, BPFP=0.7744 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,712B, BPFP=0.6522 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,824B, BPFP=0.2415 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,808B, BPFP=0.2412 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,384B, BPFP=0.1948 +⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.83065629 + text_encoder-item0.clip_prompt_embeds 0.00022609 23.87031165 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.86720915 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.11626295 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00273593 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.01130640 2.23257685 + vae.encoder_f1 0.01130902 2.23122382 + vae.decoder 0.00020860 0.04597215 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 2.29533932 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 108496 +BPFP 0.3839 bits/point +EBPFP 0.7678 equivalent bits/point +MSE 2.295339 +---------------------- -------------------------------------------------------- +Time: 3.772s Load: 0.009s, Pack+Encode: 2.162s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2953 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,664B, BPFP=0.6310 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 880B, BPFP=5.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,048B, BPFP=0.6532 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,084B, BPFP=0.5855 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,412B, BPFP=0.1284 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,416B, BPFP=0.1284 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,716B, BPFP=0.1439 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.83327031 + text_encoder-item0.clip_prompt_embeds 0.00022402 23.87856382 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 1.01564064 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.11217485 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00238347 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 1.19630027 5.33651543 + vae.encoder_f1 1.19630098 5.33478737 + vae.decoder 0.00023596 0.03710392 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 3.73380528 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 87152 +BPFP 0.3084 bits/point +EBPFP 0.6167 equivalent bits/point +MSE 3.733805 +---------------------- -------------------------------------------------------- +Time: 3.750s Load: 0.007s, Pack+Encode: 2.144s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7338 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,268B, BPFP=0.7127 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 840B, BPFP=5.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,140B, BPFP=0.7419 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,764B, BPFP=0.6535 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,160B, BPFP=0.2924 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,172B, BPFP=0.2925 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,660B, BPFP=0.3253 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.84760300 + text_encoder-item0.clip_prompt_embeds 0.00030342 23.84673887 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.80720968 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.12316440 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00270603 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00586287 1.52932823 + vae.encoder_f1 0.00587438 1.53019440 + vae.decoder 0.00017677 0.07601663 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 1.97284496 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118936 +BPFP 0.4208 bits/point +EBPFP 0.8417 equivalent bits/point +MSE 1.972845 +---------------------- -------------------------------------------------------- +Time: 3.750s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9728 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,508B, BPFP=0.6098 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 868B, BPFP=5.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,896B, BPFP=0.6409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,960B, BPFP=0.5824 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,432B, BPFP=0.2202 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,440B, BPFP=0.2203 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,748B, BPFP=0.1754 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.81513079 + text_encoder-item0.clip_prompt_embeds 0.00024120 23.82504946 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.89479332 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.11036035 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00227786 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00779453 1.83153474 + vae.encoder_f1 0.00779802 1.83182740 + vae.decoder 0.00023829 0.04389901 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 2.10799533 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 99788 +BPFP 0.3531 bits/point +EBPFP 0.7062 equivalent bits/point +MSE 2.107995 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1080 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,012B, BPFP=0.6780 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 836B, BPFP=5.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,808B, BPFP=0.7149 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,976B, BPFP=0.6082 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,136B, BPFP=0.3378 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,132B, BPFP=0.3377 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,884B, BPFP=0.2101 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.85076857 + text_encoder-item0.clip_prompt_embeds 0.00025651 23.86635256 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.86871529 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.10857085 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00254635 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00655775 1.95896149 + vae.encoder_f1 0.00656268 1.95900297 + vae.decoder 0.00020283 0.04692890 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 2.16842191 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118716 +BPFP 0.4200 bits/point +EBPFP 0.8401 equivalent bits/point +MSE 2.168422 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1684 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,256B, BPFP=0.5758 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 828B, BPFP=5.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,244B, BPFP=0.5880 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,164B, BPFP=0.5368 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,340B, BPFP=0.3256 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,364B, BPFP=0.3260 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,000B, BPFP=0.2441 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.85979358 + text_encoder-item0.clip_prompt_embeds 0.00022242 23.87803115 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.88060017 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.10052381 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00225001 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00593415 1.72519755 + vae.encoder_f1 0.00594307 1.72388017 + vae.decoder 0.00018992 0.06470372 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 2.06167852 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 113124 +BPFP 0.4003 bits/point +EBPFP 0.8005 equivalent bits/point +MSE 2.061679 +---------------------- -------------------------------------------------------- +Time: 3.767s Load: 0.009s, Pack+Encode: 2.147s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0617 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,388B, BPFP=0.7289 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,244B, BPFP=0.7503 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,800B, BPFP=0.6291 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,880B, BPFP=0.2881 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,892B, BPFP=0.2883 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,488B, BPFP=0.2285 +⌛️ [2/4] FRONTEND: Frontend time: 2.165s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.83178202 + text_encoder-item0.clip_prompt_embeds 0.00022110 23.77549843 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.85183525 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.11839837 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00263971 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00641770 1.72982085 + vae.encoder_f1 0.00642053 1.73170066 + vae.decoder 0.00017498 0.03963344 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 2.05978335 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114412 +BPFP 0.4048 bits/point +EBPFP 0.8096 equivalent bits/point +MSE 2.059783 +---------------------- -------------------------------------------------------- +Time: 3.767s Load: 0.008s, Pack+Encode: 2.165s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0598 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,292B, BPFP=0.5806 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 836B, BPFP=5.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,948B, BPFP=0.6451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,888B, BPFP=0.6313 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,660B, BPFP=0.2390 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,664B, BPFP=0.2390 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,592B, BPFP=0.2622 +⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.84371090 + text_encoder-item0.clip_prompt_embeds 0.00021654 23.85337189 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.88945980 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.11294892 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00308358 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00577698 1.51141524 + vae.encoder_f1 0.00578348 1.51252270 + vae.decoder 0.00017559 0.05942889 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 1.96249631 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 106820 +BPFP 0.3780 bits/point +EBPFP 0.7559 equivalent bits/point +MSE 1.962496 +---------------------- -------------------------------------------------------- +Time: 3.764s Load: 0.008s, Pack+Encode: 2.162s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9625 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 520B, BPFP=5.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,964B, BPFP=0.6715 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 796B, BPFP=4.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,648B, BPFP=0.7019 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,568B, BPFP=0.6485 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,640B, BPFP=0.2844 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,640B, BPFP=0.2844 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,052B, BPFP=0.1847 +⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.89337071 + text_encoder-item0.clip_prompt_embeds 0.00022160 23.86023953 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.77966385 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.10053646 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00282335 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00668450 1.67627096 + vae.encoder_f1 0.00668875 1.67985725 + vae.decoder 0.00023059 0.04887169 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 2.03785887 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 112208 +BPFP 0.3970 bits/point +EBPFP 0.7940 equivalent bits/point +MSE 2.037859 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.008s, Pack+Encode: 2.155s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0379 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 568B, BPFP=5.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,416B, BPFP=0.7327 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 776B, BPFP=4.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,964B, BPFP=0.7276 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,068B, BPFP=0.6612 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,432B, BPFP=0.2507 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,436B, BPFP=0.2508 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,172B, BPFP=0.1273 +⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.83033673 + text_encoder-item0.clip_prompt_embeds 0.00023190 23.79822147 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.73408380 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.10797548 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00294005 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.04018118 2.56911898 + vae.encoder_f1 0.04018488 2.56824017 + vae.decoder 0.00016201 0.03003616 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 2.44738536 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 107212 +BPFP 0.3793 bits/point +EBPFP 0.7587 equivalent bits/point +MSE 2.447385 +---------------------- -------------------------------------------------------- +Time: 3.767s Load: 0.009s, Pack+Encode: 2.158s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4474 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 524B, BPFP=5.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,636B, BPFP=0.6272 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,888B, BPFP=0.6403 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,524B, BPFP=0.6221 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,860B, BPFP=0.2878 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,844B, BPFP=0.2875 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,188B, BPFP=0.2499 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.90388664 + text_encoder-item0.clip_prompt_embeds 0.00023140 23.88304079 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.81231508 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.10921607 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00260228 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.04874706 2.71420360 + vae.encoder_f1 0.04875064 2.72208214 + vae.decoder 0.00019641 0.03884537 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 2.52001771 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 112652 +BPFP 0.3986 bits/point +EBPFP 0.7972 equivalent bits/point +MSE 2.520018 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.009s, Pack+Encode: 2.145s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5200 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,940B, BPFP=0.6683 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,364B, BPFP=0.6789 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,620B, BPFP=0.5738 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,108B, BPFP=0.3526 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,108B, BPFP=0.3526 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,684B, BPFP=0.1124 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.83327127 + text_encoder-item0.clip_prompt_embeds 0.00030893 23.86768212 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.84102917 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.11524017 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00238493 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.01360236 2.51335573 + vae.encoder_f1 0.01360807 2.51030731 + vae.decoder 0.00023006 0.03687144 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 2.42393107 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 115568 +BPFP 0.4089 bits/point +EBPFP 0.8178 equivalent bits/point +MSE 2.423931 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4239 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,384B, BPFP=0.7284 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,120B, BPFP=0.7403 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,772B, BPFP=0.6791 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,700B, BPFP=0.1328 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,708B, BPFP=0.1329 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,208B, BPFP=0.0674 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.85753433 + text_encoder-item0.clip_prompt_embeds 0.00024198 23.88177675 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.86356831 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.11647375 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00252041 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 1.67190456 5.24764824 + vae.encoder_f1 1.67190480 5.24779749 + vae.decoder 0.00017417 0.01783886 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 3.69100588 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 90624 +BPFP 0.3207 bits/point +EBPFP 0.6413 equivalent bits/point +MSE 3.691006 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.007s, Pack+Encode: 2.141s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6910 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,984B, BPFP=0.6742 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 780B, BPFP=4.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,688B, BPFP=0.7052 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,540B, BPFP=0.6732 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,020B, BPFP=0.3513 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,016B, BPFP=0.3512 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,952B, BPFP=0.2427 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.82514389 + text_encoder-item0.clip_prompt_embeds 0.00025129 23.85197891 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.86528921 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.10805144 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00303503 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00621760 1.93848717 + vae.encoder_f1 0.00622505 1.94073749 + vae.decoder 0.00025114 0.05572411 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 2.16011744 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 123908 +BPFP 0.4384 bits/point +EBPFP 0.8768 equivalent bits/point +MSE 2.160117 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1601 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,488B, BPFP=0.6071 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 848B, BPFP=5.3000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,084B, BPFP=0.6562 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,488B, BPFP=0.5704 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,992B, BPFP=0.3813 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,984B, BPFP=0.3812 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,060B, BPFP=0.2460 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.88287481 + text_encoder-item0.clip_prompt_embeds 0.00020838 23.78097310 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.85427189 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.10807241 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00237591 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00675961 2.29238510 + vae.encoder_f1 0.00676652 2.29024053 + vae.decoder 0.00021373 0.05880725 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 2.32164757 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 122860 +BPFP 0.4347 bits/point +EBPFP 0.8694 equivalent bits/point +MSE 2.321648 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3216 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,912B, BPFP=0.6645 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,808B, BPFP=0.7149 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,088B, BPFP=0.7125 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 10,976B, BPFP=0.1675 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 10,980B, BPFP=0.1675 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,708B, BPFP=0.4489 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.82256802 + text_encoder-item0.clip_prompt_embeds 0.00021387 23.87456245 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.83016224 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.11205436 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00278659 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00596338 1.16257071 + vae.encoder_f1 0.00596322 1.16077435 + vae.decoder 0.00018207 0.08803513 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 1.80378972 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 108184 +BPFP 0.3828 bits/point +EBPFP 0.7656 equivalent bits/point +MSE 1.803790 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8038 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,868B, BPFP=0.6585 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,276B, BPFP=0.6718 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,344B, BPFP=0.5668 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,036B, BPFP=0.1226 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,036B, BPFP=0.1226 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,260B, BPFP=0.2826 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.79396216 + text_encoder-item0.clip_prompt_embeds 0.00022138 23.86529145 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.83283739 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.11532161 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00283245 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00552804 0.88623357 + vae.encoder_f1 0.00552758 0.88619089 + vae.decoder 0.00018040 0.07049032 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 1.67390397 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 90560 +BPFP 0.3204 bits/point +EBPFP 0.6409 equivalent bits/point +MSE 1.673904 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6739 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,520B, BPFP=0.6115 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 772B, BPFP=4.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,996B, BPFP=0.6490 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,736B, BPFP=0.5767 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,236B, BPFP=0.1714 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,244B, BPFP=0.1716 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,476B, BPFP=0.1671 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.88816810 + text_encoder-item0.clip_prompt_embeds 0.00024507 23.68089658 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.89486389 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.11706748 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00249598 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00721525 1.55690527 + vae.encoder_f1 0.00721777 1.55419445 + vae.decoder 0.00018707 0.03931358 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 1.97598018 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 92916 +BPFP 0.3288 bits/point +EBPFP 0.6575 equivalent bits/point +MSE 1.975980 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.007s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9760 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,324B, BPFP=0.5850 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 836B, BPFP=5.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,136B, BPFP=0.5792 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,628B, BPFP=0.5232 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,400B, BPFP=0.2808 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,412B, BPFP=0.2809 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,568B, BPFP=0.2310 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.584s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.80998604 + text_encoder-item0.clip_prompt_embeds 0.00046272 23.86759334 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.89944487 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.10592090 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00214407 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.01999603 2.31108427 + vae.encoder_f1 0.01999529 2.30996251 + vae.decoder 0.00024882 0.05044583 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 2.33172762 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 106244 +BPFP 0.3759 bits/point +EBPFP 0.7518 equivalent bits/point +MSE 2.331728 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.584s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3317 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,408B, BPFP=0.5963 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 848B, BPFP=5.3000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,988B, BPFP=0.6484 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,272B, BPFP=0.5396 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,876B, BPFP=0.3033 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,876B, BPFP=0.3033 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,680B, BPFP=0.1428 +⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.85347859 + text_encoder-item0.clip_prompt_embeds 0.00020334 23.86152471 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.77089238 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.10375704 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00207544 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.01341345 2.26554680 + vae.encoder_f1 0.01341645 2.26532221 + vae.decoder 0.00018350 0.03078478 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 2.30821666 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 107868 +BPFP 0.3817 bits/point +EBPFP 0.7633 equivalent bits/point +MSE 2.308217 +---------------------- -------------------------------------------------------- +Time: 3.739s Load: 0.009s, Pack+Encode: 2.136s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3082 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,712B, BPFP=0.6374 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,520B, BPFP=0.6916 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,032B, BPFP=0.6349 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,860B, BPFP=0.3183 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,860B, BPFP=0.3183 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,404B, BPFP=0.3480 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.586s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.83899617 + text_encoder-item0.clip_prompt_embeds 0.00022316 23.83282814 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.86385822 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.11506623 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00278886 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00606298 1.63249540 + vae.encoder_f1 0.00607096 1.63216829 + vae.decoder 0.00023408 0.06543185 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 2.01851054 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 121112 +BPFP 0.4285 bits/point +EBPFP 0.8571 equivalent bits/point +MSE 2.018511 +---------------------- -------------------------------------------------------- +Time: 3.723s Load: 0.008s, Pack+Encode: 2.128s, Decode+Unpack: 1.586s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0185 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,580B, BPFP=0.6196 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 824B, BPFP=5.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,472B, BPFP=0.6065 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,196B, BPFP=0.6137 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,540B, BPFP=0.2982 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,528B, BPFP=0.2980 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,924B, BPFP=0.3029 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.84158524 + text_encoder-item0.clip_prompt_embeds 0.00023597 23.82383404 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.84774380 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.09653800 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00239982 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00653100 1.79484057 + vae.encoder_f1 0.00653745 1.79495299 + vae.decoder 0.00020026 0.05541398 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 2.09163606 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114984 +BPFP 0.4068 bits/point +EBPFP 0.8137 equivalent bits/point +MSE 2.091636 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.009s, Pack+Encode: 2.139s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0916 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,984B, BPFP=0.6742 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,452B, BPFP=0.6860 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,768B, BPFP=0.5775 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,164B, BPFP=0.3077 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,152B, BPFP=0.3075 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,120B, BPFP=0.1562 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.83108862 + text_encoder-item0.clip_prompt_embeds 0.00022433 23.81951772 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.86956081 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.10507975 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00241413 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00869686 2.33466864 + vae.encoder_f1 0.00870063 2.33805871 + vae.decoder 0.00021246 0.04184572 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 2.34144824 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 111368 +BPFP 0.3941 bits/point +EBPFP 0.7881 equivalent bits/point +MSE 2.341448 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3414 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,184B, BPFP=0.7013 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,976B, BPFP=0.7286 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,980B, BPFP=0.6336 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,672B, BPFP=0.3459 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,680B, BPFP=0.3461 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,620B, BPFP=0.2020 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.85156735 + text_encoder-item0.clip_prompt_embeds 0.00022433 23.86026701 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.81878805 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.10433752 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00259712 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00626512 2.13447738 + vae.encoder_f1 0.00626949 2.13307738 + vae.decoder 0.00018936 0.04572481 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 2.24898211 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 120840 +BPFP 0.4276 bits/point +EBPFP 0.8551 equivalent bits/point +MSE 2.248982 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.009s, Pack+Encode: 2.147s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2490 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,328B, BPFP=0.7208 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 824B, BPFP=5.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,096B, BPFP=0.7383 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,776B, BPFP=0.6538 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,600B, BPFP=0.2380 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,596B, BPFP=0.2380 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,964B, BPFP=0.1515 +⌛️ [2/4] FRONTEND: Frontend time: 2.160s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.94862636 + text_encoder-item0.clip_prompt_embeds 0.00026137 23.90143906 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.78788176 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.11860948 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00280564 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.35915655 3.59348392 + vae.encoder_f1 0.35915723 3.60629416 + vae.decoder 0.00024181 0.03622481 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 2.92956004 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 106112 +BPFP 0.3755 bits/point +EBPFP 0.7509 equivalent bits/point +MSE 2.929560 +---------------------- -------------------------------------------------------- +Time: 3.764s Load: 0.008s, Pack+Encode: 2.160s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9296 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,132B, BPFP=0.5590 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,104B, BPFP=0.5766 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,448B, BPFP=0.5440 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,100B, BPFP=0.1083 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,096B, BPFP=0.1083 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,340B, BPFP=0.1019 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.609s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.86648409 + text_encoder-item0.clip_prompt_embeds 0.00021656 23.83416193 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.88154650 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.10318220 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00221144 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.29031765 2.69674039 + vae.encoder_f1 0.29031771 2.72031808 + vae.decoder 0.00019965 0.04827992 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 2.51508359 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 79960 +BPFP 0.2829 bits/point +EBPFP 0.5658 equivalent bits/point +MSE 2.515084 +---------------------- -------------------------------------------------------- +Time: 3.765s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.609s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5151 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,056B, BPFP=0.5487 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,224B, BPFP=0.5864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,412B, BPFP=0.5178 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,512B, BPFP=0.2367 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,520B, BPFP=0.2368 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,968B, BPFP=0.3958 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.86361790 + text_encoder-item0.clip_prompt_embeds 0.00025451 24.08609392 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.81475792 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.10149928 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00212644 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00595764 1.25990105 + vae.encoder_f1 0.00596395 1.25871062 + vae.decoder 0.00019845 0.07239348 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 1.85224101 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 105436 +BPFP 0.3731 bits/point +EBPFP 0.7461 equivalent bits/point +MSE 1.852241 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8522 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,136B, BPFP=0.6948 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,936B, BPFP=0.7253 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,964B, BPFP=0.6586 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,248B, BPFP=0.1869 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,248B, BPFP=0.1869 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,384B, BPFP=0.1338 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.86115360 + text_encoder-item0.clip_prompt_embeds 0.00026157 23.79347817 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.85674801 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.11716515 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00293698 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.40456498 4.01680756 + vae.encoder_f1 0.40456539 4.01192045 + vae.decoder 0.00020503 0.03556418 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 3.11884465 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 98632 +BPFP 0.3490 bits/point +EBPFP 0.6980 equivalent bits/point +MSE 3.118845 +---------------------- -------------------------------------------------------- +Time: 3.726s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1188 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,956B, BPFP=0.6705 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,520B, BPFP=0.6104 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,072B, BPFP=0.5599 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,488B, BPFP=0.3126 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,492B, BPFP=0.3127 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,504B, BPFP=0.2900 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.586s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.81812358 + text_encoder-item0.clip_prompt_embeds 0.00027179 23.83814216 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.88814993 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.10115708 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00221671 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00673531 2.21116519 + vae.encoder_f1 0.00673732 2.21292996 + vae.decoder 0.00020129 0.06308398 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 2.28655151 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114776 +BPFP 0.4061 bits/point +EBPFP 0.8122 equivalent bits/point +MSE 2.286552 +---------------------- -------------------------------------------------------- +Time: 3.725s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.586s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2866 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,164B, BPFP=0.6986 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,756B, BPFP=0.7107 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,348B, BPFP=0.6683 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,024B, BPFP=0.2598 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,012B, BPFP=0.2596 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,412B, BPFP=0.1346 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.86110552 + text_encoder-item0.clip_prompt_embeds 0.00023057 23.85795666 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.87620592 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.11877416 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00283515 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00881784 2.13825560 + vae.encoder_f1 0.00882136 2.13471913 + vae.decoder 0.00017598 0.03071065 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 2.24913597 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 108460 +BPFP 0.3838 bits/point +EBPFP 0.7675 equivalent bits/point +MSE 2.249136 +---------------------- -------------------------------------------------------- +Time: 3.725s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2491 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,812B, BPFP=0.6510 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 792B, BPFP=4.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,388B, BPFP=0.6808 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,928B, BPFP=0.6069 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,128B, BPFP=0.2461 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,120B, BPFP=0.2460 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,704B, BPFP=0.2656 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.84246858 + text_encoder-item0.clip_prompt_embeds 0.00025208 23.86141902 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.85281019 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.12709424 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00264685 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00582247 1.32126212 + vae.encoder_f1 0.00582996 1.32210803 + vae.decoder 0.00016099 0.06810691 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 1.87599985 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 107804 +BPFP 0.3814 bits/point +EBPFP 0.7629 equivalent bits/point +MSE 1.876000 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8760 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 576B, BPFP=6.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,604B, BPFP=0.6228 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 796B, BPFP=4.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,128B, BPFP=0.6597 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,972B, BPFP=0.5827 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,808B, BPFP=0.3480 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,796B, BPFP=0.3478 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,960B, BPFP=0.2124 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.84359614 + text_encoder-item0.clip_prompt_embeds 0.00020809 23.82893669 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.85488710 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.11978471 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00224678 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00602745 2.09905267 + vae.encoder_f1 0.00603159 2.10350680 + vae.decoder 0.00017526 0.05372984 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 2.23466164 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118020 +BPFP 0.4176 bits/point +EBPFP 0.8352 equivalent bits/point +MSE 2.234662 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2347 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 564B, BPFP=5.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,556B, BPFP=0.6163 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,960B, BPFP=0.6461 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,428B, BPFP=0.5689 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,464B, BPFP=0.3580 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,460B, BPFP=0.3580 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,692B, BPFP=0.2958 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.79352101 + text_encoder-item0.clip_prompt_embeds 0.00020908 23.86343767 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.87245092 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.11140109 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00228995 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00634616 2.08246422 + vae.encoder_f1 0.00635208 2.08311605 + vae.decoder 0.00022721 0.05524236 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 2.22679799 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 121292 +BPFP 0.4292 bits/point +EBPFP 0.8583 equivalent bits/point +MSE 2.226798 +---------------------- -------------------------------------------------------- +Time: 3.750s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2268 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,364B, BPFP=0.5904 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 848B, BPFP=5.3000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,516B, BPFP=0.6101 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,620B, BPFP=0.5484 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 10,520B, BPFP=0.1605 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 10,528B, BPFP=0.1606 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,712B, BPFP=0.1133 +⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.86047808 + text_encoder-item0.clip_prompt_embeds 0.00022947 23.86655548 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.92292900 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.10354838 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00213873 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.05448642 1.88902473 + vae.encoder_f1 0.05448771 1.89111555 + vae.decoder 0.00017748 0.02701620 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 2.13391746 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 88040 +BPFP 0.3115 bits/point +EBPFP 0.6230 equivalent bits/point +MSE 2.133917 +---------------------- -------------------------------------------------------- +Time: 3.768s Load: 0.008s, Pack+Encode: 2.155s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1339 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,596B, BPFP=0.6218 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,232B, BPFP=0.6682 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,708B, BPFP=0.6014 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,568B, BPFP=0.2375 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,580B, BPFP=0.2377 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,432B, BPFP=0.1353 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.84439135 + text_encoder-item0.clip_prompt_embeds 0.00020169 23.83392096 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.83469687 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.10580166 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00224194 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.06876971 2.67141151 + vae.encoder_f1 0.06877109 2.65985513 + vae.decoder 0.00023999 0.02570955 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 2.49265109 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 101860 +BPFP 0.3604 bits/point +EBPFP 0.7208 equivalent bits/point +MSE 2.492651 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.007s, Pack+Encode: 2.137s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4927 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 568B, BPFP=5.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,744B, BPFP=0.6418 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,520B, BPFP=0.6916 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,632B, BPFP=0.5741 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,772B, BPFP=0.2254 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,760B, BPFP=0.2252 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,796B, BPFP=0.3295 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.81693935 + text_encoder-item0.clip_prompt_embeds 0.00025253 23.80943714 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.90380545 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.10884162 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00261749 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00595097 1.26289248 + vae.encoder_f1 0.00595882 1.26325762 + vae.decoder 0.00020134 0.07577533 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 1.84756835 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 105984 +BPFP 0.3750 bits/point +EBPFP 0.7500 equivalent bits/point +MSE 1.847568 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.009s, Pack+Encode: 2.142s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8476 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,860B, BPFP=0.6575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,600B, BPFP=0.6981 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,460B, BPFP=0.5951 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,752B, BPFP=0.1946 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,764B, BPFP=0.1948 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,912B, BPFP=0.3025 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.85106452 + text_encoder-item0.clip_prompt_embeds 0.00022201 23.76763942 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.87833796 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.12368905 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00263870 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00831743 1.68298304 + vae.encoder_f1 0.00831926 1.67834437 + vae.decoder 0.00028593 0.04657605 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 2.03740136 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 102068 +BPFP 0.3611 bits/point +EBPFP 0.7223 equivalent bits/point +MSE 2.037401 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0374 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 564B, BPFP=5.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,156B, BPFP=0.6975 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,796B, BPFP=0.7140 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,860B, BPFP=0.6559 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,508B, BPFP=0.3434 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,496B, BPFP=0.3433 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,772B, BPFP=0.2372 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.82926146 + text_encoder-item0.clip_prompt_embeds 0.00026808 23.69417106 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.88939524 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.11118768 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00280324 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00606586 2.09060049 + vae.encoder_f1 0.00607066 2.09040308 + vae.decoder 0.00019664 0.04843540 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 2.22524207 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 122340 +BPFP 0.4329 bits/point +EBPFP 0.8657 equivalent bits/point +MSE 2.225242 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2252 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 508B, BPFP=5.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,192B, BPFP=0.7024 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,344B, BPFP=0.7584 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,176B, BPFP=0.6640 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,296B, BPFP=0.2639 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,300B, BPFP=0.2640 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,136B, BPFP=0.1873 +⌛️ [2/4] FRONTEND: Frontend time: 2.167s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.87524303 + text_encoder-item0.clip_prompt_embeds 0.00023198 23.88777352 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.92565374 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.11598617 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00278255 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.05216765 2.57749414 + vae.encoder_f1 0.05216896 2.57889390 + vae.decoder 0.00017960 0.04614798 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 2.45645906 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 111120 +BPFP 0.3932 bits/point +EBPFP 0.7863 equivalent bits/point +MSE 2.456459 +---------------------- -------------------------------------------------------- +Time: 3.770s Load: 0.008s, Pack+Encode: 2.167s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4565 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,828B, BPFP=0.6531 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 832B, BPFP=5.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,344B, BPFP=0.6773 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,032B, BPFP=0.5588 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,844B, BPFP=0.3638 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,832B, BPFP=0.3636 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,924B, BPFP=0.2113 +⌛️ [2/4] FRONTEND: Frontend time: 2.159s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.84631562 + text_encoder-item0.clip_prompt_embeds 0.00023125 23.81021079 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.89352951 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.11355732 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00233871 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00620361 1.87585378 + vae.encoder_f1 0.00620966 1.87887168 + vae.decoder 0.00020748 0.05436635 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 2.13016425 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 119560 +BPFP 0.4230 bits/point +EBPFP 0.8461 equivalent bits/point +MSE 2.130164 +---------------------- -------------------------------------------------------- +Time: 3.768s Load: 0.009s, Pack+Encode: 2.159s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1302 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,868B, BPFP=0.6585 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 772B, BPFP=4.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,568B, BPFP=0.6955 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,488B, BPFP=0.5958 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,712B, BPFP=0.3008 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,716B, BPFP=0.3008 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,596B, BPFP=0.2623 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.81775260 + text_encoder-item0.clip_prompt_embeds 0.00023066 23.87635916 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.81223221 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.10868941 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00248118 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.03159856 2.04221058 + vae.encoder_f1 0.03160188 2.03632188 + vae.decoder 0.00018417 0.04994386 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 2.20621924 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114660 +BPFP 0.4057 bits/point +EBPFP 0.8114 equivalent bits/point +MSE 2.206219 +---------------------- -------------------------------------------------------- +Time: 3.761s Load: 0.009s, Pack+Encode: 2.149s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2062 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,892B, BPFP=0.6618 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 760B, BPFP=4.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,228B, BPFP=0.6679 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,884B, BPFP=0.6312 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,000B, BPFP=0.3815 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,000B, BPFP=0.3815 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,180B, BPFP=0.1886 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.81913519 + text_encoder-item0.clip_prompt_embeds 0.00024948 23.84188777 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.82428102 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.11033662 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00258513 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.03490865 3.06251311 + vae.encoder_f1 0.03491008 3.07223701 + vae.decoder 0.00028462 0.05688715 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 2.68302035 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 123880 +BPFP 0.4383 bits/point +EBPFP 0.8766 equivalent bits/point +MSE 2.683020 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6830 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,360B, BPFP=0.5898 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 780B, BPFP=4.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,108B, BPFP=0.6581 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,632B, BPFP=0.6755 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,160B, BPFP=0.1398 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,156B, BPFP=0.1397 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,700B, BPFP=0.2960 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.81055625 + text_encoder-item0.clip_prompt_embeds 0.00021560 23.86761660 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.81359434 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.11391029 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00264211 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00544735 0.96533358 + vae.encoder_f1 0.00544843 0.96576279 + vae.decoder 0.00018632 0.07063036 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 1.71068118 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 96828 +BPFP 0.3426 bits/point +EBPFP 0.6852 equivalent bits/point +MSE 1.710681 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.009s, Pack+Encode: 2.150s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7107 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,564B, BPFP=0.6174 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 796B, BPFP=4.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,256B, BPFP=0.6701 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,852B, BPFP=0.5796 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,748B, BPFP=0.3166 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,748B, BPFP=0.3166 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,800B, BPFP=0.2686 +⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.82143164 + text_encoder-item0.clip_prompt_embeds 0.00022698 23.83763909 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.89304085 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.10548583 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00230795 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00630479 1.72101164 + vae.encoder_f1 0.00631430 1.72081423 + vae.decoder 0.00018596 0.04986764 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 2.05743876 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 115684 +BPFP 0.4093 bits/point +EBPFP 0.8186 equivalent bits/point +MSE 2.057439 +---------------------- -------------------------------------------------------- +Time: 3.764s Load: 0.009s, Pack+Encode: 2.158s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0574 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,520B, BPFP=0.6115 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 844B, BPFP=5.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,960B, BPFP=0.6461 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,944B, BPFP=0.5312 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,216B, BPFP=0.2932 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,200B, BPFP=0.2930 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,984B, BPFP=0.3047 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.89633878 + text_encoder-item0.clip_prompt_embeds 0.00024643 23.85439495 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.90740814 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.10691226 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00243166 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00612578 1.57670975 + vae.encoder_f1 0.00613243 1.57759356 + vae.decoder 0.00018179 0.05700742 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 1.99214592 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 111596 +BPFP 0.3949 bits/point +EBPFP 0.7897 equivalent bits/point +MSE 1.992146 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9921 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,676B, BPFP=0.7679 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,848B, BPFP=0.8805 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,036B, BPFP=0.7111 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 4,964B, BPFP=0.0757 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 4,964B, BPFP=0.0757 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,248B, BPFP=0.3127 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.84829632 + text_encoder-item0.clip_prompt_embeds 0.00024049 23.84893085 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.88501120 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.11812148 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00306104 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00526071 0.47408646 + vae.encoder_f1 0.00526072 0.47405764 + vae.decoder 0.00016981 0.06622580 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 1.48204608 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 94464 +BPFP 0.3342 bits/point +EBPFP 0.6685 equivalent bits/point +MSE 1.482046 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.4820 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,588B, BPFP=0.6207 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,020B, BPFP=0.6510 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,796B, BPFP=0.5782 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,984B, BPFP=0.3202 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,992B, BPFP=0.3203 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,456B, BPFP=0.3191 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.78728628 + text_encoder-item0.clip_prompt_embeds 0.00022843 23.81535571 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.85366268 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.10225165 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00243368 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00622977 1.77213204 + vae.encoder_f1 0.00623684 1.77645731 + vae.decoder 0.00019755 0.05546091 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 2.08210384 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 117564 +BPFP 0.4160 bits/point +EBPFP 0.8319 equivalent bits/point +MSE 2.082104 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.009s, Pack+Encode: 2.142s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0821 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,968B, BPFP=0.6721 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,092B, BPFP=0.6568 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,060B, BPFP=0.5849 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,100B, BPFP=0.2151 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,100B, BPFP=0.2151 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,368B, BPFP=0.1943 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.88458975 + text_encoder-item0.clip_prompt_embeds 0.00026004 23.88727256 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.93533411 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.10601220 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00247532 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00725303 1.37044764 + vae.encoder_f1 0.00725507 1.36633348 + vae.decoder 0.00017991 0.03903699 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 1.89408424 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 100428 +BPFP 0.3553 bits/point +EBPFP 0.7107 equivalent bits/point +MSE 1.894084 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8941 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,924B, BPFP=0.6661 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 852B, BPFP=5.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,580B, BPFP=0.6964 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,936B, BPFP=0.6071 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,748B, BPFP=0.2403 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,756B, BPFP=0.2404 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,432B, BPFP=0.1353 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.89151080 + text_encoder-item0.clip_prompt_embeds 0.00031748 23.82373258 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.91414862 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.12005825 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00252777 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.42111695 3.28145599 + vae.encoder_f1 0.42111716 3.28101444 + vae.decoder 0.00019827 0.03377866 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 2.77953902 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 103160 +BPFP 0.3650 bits/point +EBPFP 0.7300 equivalent bits/point +MSE 2.779539 +---------------------- -------------------------------------------------------- +Time: 3.750s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7795 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,144B, BPFP=0.6959 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 824B, BPFP=5.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,008B, BPFP=0.7312 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,940B, BPFP=0.6833 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,140B, BPFP=0.2768 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,144B, BPFP=0.2769 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,616B, BPFP=0.1714 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.80660391 + text_encoder-item0.clip_prompt_embeds 0.00024951 23.86809642 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.80819740 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.10573315 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00288148 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.10376993 3.59593558 + vae.encoder_f1 0.10377157 3.59168553 + vae.decoder 0.00019787 0.03864446 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 2.92556204 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 112740 +BPFP 0.3989 bits/point +EBPFP 0.7978 equivalent bits/point +MSE 2.925562 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9256 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,744B, BPFP=0.6418 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,584B, BPFP=0.6968 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,196B, BPFP=0.5884 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,348B, BPFP=0.2800 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,340B, BPFP=0.2798 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,508B, BPFP=0.1681 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.87016829 + text_encoder-item0.clip_prompt_embeds 0.00022350 23.66391031 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.82552557 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.11215879 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00230287 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.01346414 2.26896644 + vae.encoder_f1 0.01346933 2.26732516 + vae.decoder 0.00019243 0.03726929 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 2.30549188 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 108444 +BPFP 0.3837 bits/point +EBPFP 0.7674 equivalent bits/point +MSE 2.305492 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3055 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,824B, BPFP=0.6526 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 828B, BPFP=5.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,732B, BPFP=0.6276 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,040B, BPFP=0.6605 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,092B, BPFP=0.2455 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,096B, BPFP=0.2456 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,332B, BPFP=0.1322 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.83262197 + text_encoder-item0.clip_prompt_embeds 0.00024958 23.86288809 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.93778582 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.11261843 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00278923 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.11196710 2.98739457 + vae.encoder_f1 0.11196851 2.98860264 + vae.decoder 0.00023459 0.03995568 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 2.64499105 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 104864 +BPFP 0.3710 bits/point +EBPFP 0.7421 equivalent bits/point +MSE 2.644991 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.008s, Pack+Encode: 2.161s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6450 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,212B, BPFP=0.7051 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,780B, BPFP=0.7127 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,444B, BPFP=0.5693 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,036B, BPFP=0.3362 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,028B, BPFP=0.3361 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,528B, BPFP=0.1687 +⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.584s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.86567243 + text_encoder-item0.clip_prompt_embeds 0.00025929 23.89005428 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.87604914 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.13376304 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00217431 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00675017 2.06889892 + vae.encoder_f1 0.00675421 2.06949663 + vae.decoder 0.00023635 0.05259768 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 2.22186901 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 115772 +BPFP 0.4096 bits/point +EBPFP 0.8193 equivalent bits/point +MSE 2.221869 +---------------------- -------------------------------------------------------- +Time: 3.728s Load: 0.008s, Pack+Encode: 2.135s, Decode+Unpack: 1.584s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2219 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 564B, BPFP=5.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,596B, BPFP=0.6218 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,988B, BPFP=0.6484 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,304B, BPFP=0.6418 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,360B, BPFP=0.4022 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,360B, BPFP=0.4022 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,584B, BPFP=0.1704 +⌛️ [2/4] FRONTEND: Frontend time: 2.133s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.79724352 + text_encoder-item0.clip_prompt_embeds 0.00064775 23.86399993 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.86867237 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.10379906 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00277175 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00728993 2.57273340 + vae.encoder_f1 0.00729572 2.57288599 + vae.decoder 0.00026488 0.05151551 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 2.45337099 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 125956 +BPFP 0.4457 bits/point +EBPFP 0.8913 equivalent bits/point +MSE 2.453371 +---------------------- -------------------------------------------------------- +Time: 3.735s Load: 0.009s, Pack+Encode: 2.133s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4534 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,712B, BPFP=0.6374 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,524B, BPFP=0.6919 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,976B, BPFP=0.6335 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,580B, BPFP=0.3293 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,592B, BPFP=0.3295 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,708B, BPFP=0.2047 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.86993941 + text_encoder-item0.clip_prompt_embeds 0.00023188 23.79517976 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.84015083 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.10951506 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00259632 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00613207 1.95740008 + vae.encoder_f1 0.00613899 1.95685625 + vae.decoder 0.00023812 0.05253877 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 2.16638943 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 117828 +BPFP 0.4169 bits/point +EBPFP 0.8338 equivalent bits/point +MSE 2.166389 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1664 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,120B, BPFP=0.6926 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,680B, BPFP=0.7045 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,064B, BPFP=0.6104 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,828B, BPFP=0.2873 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,844B, BPFP=0.2875 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,448B, BPFP=0.2273 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.82205447 + text_encoder-item0.clip_prompt_embeds 0.00023678 23.81970373 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.91614742 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.10901198 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00246406 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00636537 1.97112763 + vae.encoder_f1 0.00636991 1.96216762 + vae.decoder 0.00025538 0.04914599 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 2.17103868 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 112716 +BPFP 0.3988 bits/point +EBPFP 0.7976 equivalent bits/point +MSE 2.171039 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.009s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1710 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,476B, BPFP=0.6055 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,732B, BPFP=0.6276 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,760B, BPFP=0.5519 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,280B, BPFP=0.2789 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,276B, BPFP=0.2789 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,396B, BPFP=0.1342 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.87674459 + text_encoder-item0.clip_prompt_embeds 0.00023432 23.64254642 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.76246672 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.10463917 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00248310 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.23155926 3.41490650 + vae.encoder_f1 0.23156048 3.42219496 + vae.decoder 0.00018572 0.03526046 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 2.83788522 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 104640 +BPFP 0.3702 bits/point +EBPFP 0.7405 equivalent bits/point +MSE 2.837885 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8379 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,504B, BPFP=0.6093 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,760B, BPFP=0.6299 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,136B, BPFP=0.5869 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,600B, BPFP=0.3448 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,608B, BPFP=0.3450 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,664B, BPFP=0.2339 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.84665179 + text_encoder-item0.clip_prompt_embeds 0.00022528 23.85208249 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.86906490 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.10725464 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00236752 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00729824 2.12695503 + vae.encoder_f1 0.00730369 2.12878275 + vae.decoder 0.00019938 0.06144282 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 2.24797218 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118000 +BPFP 0.4175 bits/point +EBPFP 0.8350 equivalent bits/point +MSE 2.247972 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2480 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,492B, BPFP=0.6077 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,932B, BPFP=0.6438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,192B, BPFP=0.5375 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,448B, BPFP=0.2205 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,448B, BPFP=0.2205 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,892B, BPFP=0.3019 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.81944537 + text_encoder-item0.clip_prompt_embeds 0.00022149 23.79888731 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.85795994 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.12099975 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00220059 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00564371 1.38803446 + vae.encoder_f1 0.00565042 1.38727534 + vae.decoder 0.00019980 0.06874166 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 1.90469981 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 102148 +BPFP 0.3614 bits/point +EBPFP 0.7229 equivalent bits/point +MSE 1.904700 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9047 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,016B, BPFP=0.6786 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,576B, BPFP=0.6961 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,980B, BPFP=0.5575 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,628B, BPFP=0.2385 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,640B, BPFP=0.2386 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,616B, BPFP=0.3240 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.82752085 + text_encoder-item0.clip_prompt_embeds 0.00022173 23.84761609 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.86172676 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.12256636 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00232320 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00576096 1.25992489 + vae.encoder_f1 0.00576981 1.25845253 + vae.decoder 0.00019592 0.06197868 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 1.84570194 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 107184 +BPFP 0.3792 bits/point +EBPFP 0.7585 equivalent bits/point +MSE 1.845702 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8457 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,504B, BPFP=0.7446 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,560B, BPFP=0.7760 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,660B, BPFP=0.6762 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,824B, BPFP=0.1957 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,836B, BPFP=0.1959 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,048B, BPFP=0.1541 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.87499952 + text_encoder-item0.clip_prompt_embeds 0.00025917 23.89076451 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.88335619 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.12153882 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00293281 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00594818 1.41173971 + vae.encoder_f1 0.00595328 1.41017866 + vae.decoder 0.00023462 0.04507460 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 1.91532551 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 102148 +BPFP 0.3614 bits/point +EBPFP 0.7229 equivalent bits/point +MSE 1.915326 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.007s, Pack+Encode: 2.153s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9153 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,668B, BPFP=0.7668 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,832B, BPFP=0.8792 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,876B, BPFP=0.7071 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,032B, BPFP=0.1836 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,028B, BPFP=0.1835 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,956B, BPFP=0.1207 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.79689956 + text_encoder-item0.clip_prompt_embeds 0.00022579 23.79212747 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.84955587 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.12068535 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00289565 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.85445058 4.50288010 + vae.encoder_f1 0.85445166 4.50870657 + vae.decoder 0.00025257 0.02084281 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 3.34513354 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 102132 +BPFP 0.3614 bits/point +EBPFP 0.7227 equivalent bits/point +MSE 3.345134 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3451 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,720B, BPFP=0.6385 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,236B, BPFP=0.6685 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,840B, BPFP=0.5793 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,080B, BPFP=0.3827 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,076B, BPFP=0.3826 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,440B, BPFP=0.3186 +⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.85892725 + text_encoder-item0.clip_prompt_embeds 0.00025458 23.89327778 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.87224464 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.10994049 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00224977 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00628510 1.98974931 + vae.encoder_f1 0.00629234 1.99006224 + vae.decoder 0.00023521 0.06762198 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 2.18588979 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 126128 +BPFP 0.4463 bits/point +EBPFP 0.8925 equivalent bits/point +MSE 2.185890 +---------------------- -------------------------------------------------------- +Time: 3.766s Load: 0.009s, Pack+Encode: 2.154s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1859 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,620B, BPFP=0.6250 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 832B, BPFP=5.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,016B, BPFP=0.6506 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,128B, BPFP=0.6120 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,208B, BPFP=0.2473 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,212B, BPFP=0.2474 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,096B, BPFP=0.2471 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.88652237 + text_encoder-item0.clip_prompt_embeds 0.00022807 23.87192023 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.93182621 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.10664738 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00267171 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00573429 1.39480186 + vae.encoder_f1 0.00574192 1.39376414 + vae.decoder 0.00017875 0.05230338 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 1.90728267 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 107028 +BPFP 0.3787 bits/point +EBPFP 0.7574 equivalent bits/point +MSE 1.907283 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9073 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,464B, BPFP=0.8745 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,156B, BPFP=0.9867 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,412B, BPFP=1.2533 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,528B, BPFP=0.3285 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,512B, BPFP=0.3282 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,156B, BPFP=0.2489 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.86784180 + text_encoder-item0.clip_prompt_embeds 0.00027120 23.89640617 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.86646910 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.13512083 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00450018 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00781570 2.31340528 + vae.encoder_f1 0.00781878 2.31722593 + vae.decoder 0.00029724 0.06009343 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 2.33742476 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 148944 +BPFP 0.5270 bits/point +EBPFP 1.0540 equivalent bits/point +MSE 2.337425 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3374 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 572B, BPFP=5.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,908B, BPFP=0.6640 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 776B, BPFP=4.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,896B, BPFP=0.7221 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,856B, BPFP=0.6051 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,480B, BPFP=0.2972 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,488B, BPFP=0.2974 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,584B, BPFP=0.3840 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.86577765 + text_encoder-item0.clip_prompt_embeds 0.00022930 23.79028215 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.79752951 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.11036105 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00242895 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00577752 1.44430304 + vae.encoder_f1 0.00578475 1.44395792 + vae.decoder 0.00024190 0.06737972 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 1.93005804 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118940 +BPFP 0.4208 bits/point +EBPFP 0.8417 equivalent bits/point +MSE 1.930058 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9301 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,680B, BPFP=0.7684 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 824B, BPFP=5.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,352B, BPFP=0.7591 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,604B, BPFP=0.7002 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,504B, BPFP=0.3281 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,492B, BPFP=0.3279 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 3,860B, BPFP=0.1178 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.91702930 + text_encoder-item0.clip_prompt_embeds 0.00028764 23.78179535 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.89763489 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.10751669 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00283596 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.03343784 3.06175947 + vae.encoder_f1 0.03344063 3.06419373 + vae.decoder 0.00016139 0.02751608 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 2.67599027 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 119248 +BPFP 0.4219 bits/point +EBPFP 0.8439 equivalent bits/point +MSE 2.675990 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.009s, Pack+Encode: 2.149s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6760 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 568B, BPFP=5.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,428B, BPFP=0.5990 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,160B, BPFP=0.6623 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,116B, BPFP=0.5863 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,176B, BPFP=0.3842 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,148B, BPFP=0.3837 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,400B, BPFP=0.2869 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.81506666 + text_encoder-item0.clip_prompt_embeds 0.00023094 23.88841188 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.83805981 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.10716456 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00254681 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00637455 2.02687764 + vae.encoder_f1 0.00637988 2.02865243 + vae.decoder 0.00020059 0.06053424 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 2.20238485 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 125192 +BPFP 0.4430 bits/point +EBPFP 0.8859 equivalent bits/point +MSE 2.202385 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2024 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 568B, BPFP=5.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,996B, BPFP=0.6759 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 832B, BPFP=5.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,680B, BPFP=0.7045 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,664B, BPFP=0.6510 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,848B, BPFP=0.2876 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,860B, BPFP=0.2878 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,316B, BPFP=0.2843 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.88210058 + text_encoder-item0.clip_prompt_embeds 0.00025217 23.80840351 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.94590969 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.10911267 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00293017 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00581597 1.45285082 + vae.encoder_f1 0.00582356 1.45070982 + vae.decoder 0.00019494 0.05927231 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 1.93324480 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 116144 +BPFP 0.4109 bits/point +EBPFP 0.8219 equivalent bits/point +MSE 1.933245 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9332 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 524B, BPFP=5.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,256B, BPFP=0.5758 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 6,980B, BPFP=0.5666 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,776B, BPFP=0.5524 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 10,896B, BPFP=0.1663 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 10,896B, BPFP=0.1663 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,288B, BPFP=0.1614 +⌛️ [2/4] FRONTEND: Frontend time: 2.133s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.89496152 + text_encoder-item0.clip_prompt_embeds 0.00026975 23.88629600 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.88568592 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.11179326 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00215576 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 1.11695218 4.51543188 + vae.encoder_f1 1.11695278 4.49766922 + vae.decoder 0.00019720 0.04865539 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 3.35073526 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 89812 +BPFP 0.3178 bits/point +EBPFP 0.6356 equivalent bits/point +MSE 3.350735 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.007s, Pack+Encode: 2.133s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3507 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,884B, BPFP=0.6607 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,072B, BPFP=0.6552 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,680B, BPFP=0.6514 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,416B, BPFP=0.2963 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,436B, BPFP=0.2966 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,244B, BPFP=0.2516 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.84131535 + text_encoder-item0.clip_prompt_embeds 0.00025545 23.79525374 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.76980619 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.10998962 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00260895 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.01535016 2.41759777 + vae.encoder_f1 0.01535382 2.41793728 + vae.decoder 0.00021460 0.05269147 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 2.38001182 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 115464 +BPFP 0.4085 bits/point +EBPFP 0.8171 equivalent bits/point +MSE 2.380012 +---------------------- -------------------------------------------------------- +Time: 3.766s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3800 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,256B, BPFP=0.7110 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,292B, BPFP=0.7542 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,128B, BPFP=0.6627 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,220B, BPFP=0.2322 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,212B, BPFP=0.2321 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,012B, BPFP=0.3361 +⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.84618576 + text_encoder-item0.clip_prompt_embeds 0.00022628 23.79591746 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.90268078 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.11732530 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00276397 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00589589 1.24995816 + vae.encoder_f1 0.00590398 1.24855781 + vae.decoder 0.00017838 0.07825442 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 1.84149380 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 111832 +BPFP 0.3957 bits/point +EBPFP 0.7914 equivalent bits/point +MSE 1.841494 +---------------------- -------------------------------------------------------- +Time: 3.773s Load: 0.010s, Pack+Encode: 2.163s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8415 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,604B, BPFP=0.6228 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 796B, BPFP=4.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,028B, BPFP=0.6516 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 21,872B, BPFP=0.5548 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,528B, BPFP=0.3590 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,532B, BPFP=0.3591 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,824B, BPFP=0.1472 +⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.82687449 + text_encoder-item0.clip_prompt_embeds 0.00031548 23.78195389 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.86474438 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.11164038 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00215357 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00725484 2.50404501 + vae.encoder_f1 0.00725992 2.50725889 + vae.decoder 0.00019960 0.04165804 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 2.41919999 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 116124 +BPFP 0.4109 bits/point +EBPFP 0.8218 equivalent bits/point +MSE 2.419200 +---------------------- -------------------------------------------------------- +Time: 3.772s Load: 0.009s, Pack+Encode: 2.162s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4192 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,204B, BPFP=0.5687 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 872B, BPFP=5.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 6,868B, BPFP=0.5575 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 20,064B, BPFP=0.5089 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,176B, BPFP=0.3079 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,172B, BPFP=0.3078 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,200B, BPFP=0.1892 +⌛️ [2/4] FRONTEND: Frontend time: 2.157s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.84536695 + text_encoder-item0.clip_prompt_embeds 0.00021831 23.88167529 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 1.01574459 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.10301062 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00194961 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00923516 2.21725321 + vae.encoder_f1 0.00923823 2.21425867 + vae.decoder 0.00019521 0.03566433 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 2.28635588 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 107484 +BPFP 0.3803 bits/point +EBPFP 0.7606 equivalent bits/point +MSE 2.286356 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.008s, Pack+Encode: 2.157s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2864 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 520B, BPFP=5.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,696B, BPFP=0.6353 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,344B, BPFP=0.6773 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,736B, BPFP=0.6274 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,648B, BPFP=0.3303 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,672B, BPFP=0.3307 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,904B, BPFP=0.2107 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.85955516 + text_encoder-item0.clip_prompt_embeds 0.00062166 23.80995925 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.84212265 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.10869741 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00253471 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00831779 2.25304627 + vae.encoder_f1 0.00832197 2.25870442 + vae.decoder 0.00023271 0.04630571 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 2.30455608 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 117700 +BPFP 0.4165 bits/point +EBPFP 0.8329 equivalent bits/point +MSE 2.304556 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3046 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,700B, BPFP=0.6358 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 784B, BPFP=4.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,900B, BPFP=0.6412 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,188B, BPFP=0.6135 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,696B, BPFP=0.2700 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,684B, BPFP=0.2698 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,556B, BPFP=0.1390 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.85385847 + text_encoder-item0.clip_prompt_embeds 0.00022938 23.86612850 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.84596195 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.10649480 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00259210 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00626977 1.64644837 + vae.encoder_f1 0.00627489 1.64967585 + vae.decoder 0.00017842 0.05190464 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 2.02470216 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 106432 +BPFP 0.3766 bits/point +EBPFP 0.7532 equivalent bits/point +MSE 2.024702 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.009s, Pack+Encode: 2.138s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0247 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,984B, BPFP=0.6742 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,876B, BPFP=0.7205 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,580B, BPFP=0.6235 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,340B, BPFP=0.2798 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,352B, BPFP=0.2800 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,676B, BPFP=0.2953 +⌛️ [2/4] FRONTEND: Frontend time: 2.165s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.80110741 + text_encoder-item0.clip_prompt_embeds 0.00022180 23.89366672 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.82348394 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.11178571 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00278335 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00585720 1.50724125 + vae.encoder_f1 0.00586586 1.50810945 + vae.decoder 0.00016520 0.07080744 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 1.96273382 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114552 +BPFP 0.4053 bits/point +EBPFP 0.8106 equivalent bits/point +MSE 1.962734 +---------------------- -------------------------------------------------------- +Time: 3.779s Load: 0.009s, Pack+Encode: 2.165s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9627 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,656B, BPFP=0.6299 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 784B, BPFP=4.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,196B, BPFP=0.6653 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,476B, BPFP=0.5955 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,140B, BPFP=0.2310 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,132B, BPFP=0.2309 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,436B, BPFP=0.1964 +⌛️ [2/4] FRONTEND: Frontend time: 2.165s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.85782488 + text_encoder-item0.clip_prompt_embeds 0.00025784 23.86219266 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.72089043 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.10208406 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00249874 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00734802 1.80048048 + vae.encoder_f1 0.00734987 1.80229974 + vae.decoder 0.00018093 0.03117982 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 2.09303023 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 102748 +BPFP 0.3636 bits/point +EBPFP 0.7271 equivalent bits/point +MSE 2.093030 +---------------------- -------------------------------------------------------- +Time: 3.766s Load: 0.008s, Pack+Encode: 2.165s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0930 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,196B, BPFP=0.7029 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,540B, BPFP=0.6932 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,788B, BPFP=0.6034 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,124B, BPFP=0.3681 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,128B, BPFP=0.3682 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,708B, BPFP=0.2047 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.84612719 + text_encoder-item0.clip_prompt_embeds 0.00023510 23.88338110 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.82510290 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.11328020 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00252396 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00637359 2.02584791 + vae.encoder_f1 0.00637830 2.02433681 + vae.decoder 0.00018566 0.05852472 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 2.20104740 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 122216 +BPFP 0.4324 bits/point +EBPFP 0.8649 equivalent bits/point +MSE 2.201047 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2010 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,648B, BPFP=0.6288 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 792B, BPFP=4.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,616B, BPFP=0.6994 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,424B, BPFP=0.6195 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,136B, BPFP=0.2615 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,124B, BPFP=0.2613 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,108B, BPFP=0.1254 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.85656230 + text_encoder-item0.clip_prompt_embeds 0.00026418 23.86710506 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.84959106 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.11786935 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00258271 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.01530954 2.36900806 + vae.encoder_f1 0.01531230 2.36636662 + vae.decoder 0.00017892 0.03581875 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 2.35709941 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 105768 +BPFP 0.3742 bits/point +EBPFP 0.7485 equivalent bits/point +MSE 2.357099 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3571 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,276B, BPFP=0.7137 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,784B, BPFP=0.7130 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,380B, BPFP=0.6438 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,280B, BPFP=0.3247 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,280B, BPFP=0.3247 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,260B, BPFP=0.2521 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.80594444 + text_encoder-item0.clip_prompt_embeds 0.00021481 23.85526371 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.76831908 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.10204204 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00293791 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00591154 2.05325294 + vae.encoder_f1 0.00591973 2.05202270 + vae.decoder 0.00025286 0.05627364 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 2.21234776 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 119988 +BPFP 0.4245 bits/point +EBPFP 0.8491 equivalent bits/point +MSE 2.212348 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2123 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,380B, BPFP=0.7278 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,368B, BPFP=0.7604 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,444B, BPFP=0.6961 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,036B, BPFP=0.1989 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,024B, BPFP=0.1987 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,868B, BPFP=0.3011 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.86000013 + text_encoder-item0.clip_prompt_embeds 0.00023458 23.80487563 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.87244720 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.12158368 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00308179 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00588703 1.11888659 + vae.encoder_f1 0.00589573 1.12214541 + vae.decoder 0.00053402 0.06059280 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 1.78019149 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 107836 +BPFP 0.3816 bits/point +EBPFP 0.7631 equivalent bits/point +MSE 1.780191 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.009s, Pack+Encode: 2.139s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7802 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,320B, BPFP=0.7197 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,976B, BPFP=0.7286 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,948B, BPFP=0.6582 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,852B, BPFP=0.3029 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,828B, BPFP=0.3026 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,556B, BPFP=0.1696 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.80988900 + text_encoder-item0.clip_prompt_embeds 0.00022882 23.85597183 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.89758492 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.11316407 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00266589 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00659691 2.01204205 + vae.encoder_f1 0.00660300 2.01439023 + vae.decoder 0.00023739 0.03950044 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 2.19266045 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 115228 +BPFP 0.4077 bits/point +EBPFP 0.8154 equivalent bits/point +MSE 2.192660 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1927 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,856B, BPFP=0.6569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 824B, BPFP=5.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,324B, BPFP=0.6756 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,028B, BPFP=0.5841 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,848B, BPFP=0.1960 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,848B, BPFP=0.1960 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,484B, BPFP=0.3199 +⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.85501154 + text_encoder-item0.clip_prompt_embeds 0.00023928 23.86912583 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.86221590 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.10139849 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00244076 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00583864 1.23455906 + vae.encoder_f1 0.00583800 1.23455381 + vae.decoder 0.00018889 0.07040647 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 1.83492127 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 102128 +BPFP 0.3614 bits/point +EBPFP 0.7227 equivalent bits/point +MSE 1.834921 +---------------------- -------------------------------------------------------- +Time: 3.764s Load: 0.008s, Pack+Encode: 2.158s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8349 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,144B, BPFP=0.6959 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 780B, BPFP=4.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,880B, BPFP=0.7208 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,108B, BPFP=0.7130 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 10,920B, BPFP=0.1666 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 10,924B, BPFP=0.1667 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,064B, BPFP=0.2156 +⌛️ [2/4] FRONTEND: Frontend time: 2.156s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.87523301 + text_encoder-item0.clip_prompt_embeds 0.00024821 23.87079782 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.68934870 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.11116969 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00336807 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00570467 0.98040038 + vae.encoder_f1 0.00570488 0.98036331 + vae.decoder 0.00017302 0.05788405 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 1.71609936 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 100740 +BPFP 0.3564 bits/point +EBPFP 0.7129 equivalent bits/point +MSE 1.716099 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.009s, Pack+Encode: 2.156s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7161 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,132B, BPFP=0.6943 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,664B, BPFP=0.7032 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,196B, BPFP=0.6137 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,928B, BPFP=0.1820 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,924B, BPFP=0.1819 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,104B, BPFP=0.1252 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.85147603 + text_encoder-item0.clip_prompt_embeds 0.00021458 23.79433425 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.74903307 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.10901402 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00253028 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00914783 1.98385429 + vae.encoder_f1 0.00914958 1.98993611 + vae.decoder 0.00017527 0.03275422 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 2.17778954 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 95676 +BPFP 0.3385 bits/point +EBPFP 0.6771 equivalent bits/point +MSE 2.177790 +---------------------- -------------------------------------------------------- +Time: 3.765s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1778 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,748B, BPFP=0.7776 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 780B, BPFP=4.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,372B, BPFP=0.8419 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,592B, BPFP=0.7252 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,944B, BPFP=0.2128 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,944B, BPFP=0.2128 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,904B, BPFP=0.3633 +⌛️ [2/4] FRONTEND: Frontend time: 2.157s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.84658464 + text_encoder-item0.clip_prompt_embeds 0.00022150 23.86567404 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.89634161 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.11764080 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00277769 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00578482 1.18322086 + vae.encoder_f1 0.00579739 1.18316317 + vae.decoder 0.00017668 0.07167547 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 1.81192842 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114224 +BPFP 0.4042 bits/point +EBPFP 0.8083 equivalent bits/point +MSE 1.811928 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.009s, Pack+Encode: 2.157s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8119 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 568B, BPFP=5.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,760B, BPFP=0.6439 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,096B, BPFP=0.6571 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,036B, BPFP=0.5843 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,084B, BPFP=0.2302 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,088B, BPFP=0.2302 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,496B, BPFP=0.2288 +⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.85621293 + text_encoder-item0.clip_prompt_embeds 0.00023894 23.78523657 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.85915022 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.10646548 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00226350 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00958025 1.82702529 + vae.encoder_f1 0.00958229 1.82239795 + vae.decoder 0.00019995 0.05499873 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 2.10483074 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 103308 +BPFP 0.3655 bits/point +EBPFP 0.7311 equivalent bits/point +MSE 2.104831 +---------------------- -------------------------------------------------------- +Time: 3.765s Load: 0.008s, Pack+Encode: 2.163s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1048 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 516B, BPFP=5.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,684B, BPFP=0.7689 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,436B, BPFP=0.8471 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,784B, BPFP=0.7301 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,428B, BPFP=0.1439 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,424B, BPFP=0.1438 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,560B, BPFP=0.3528 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.84457429 + text_encoder-item0.clip_prompt_embeds 0.00023387 23.85029846 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.84625320 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.12610566 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00366840 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00567713 1.04932296 + vae.encoder_f1 0.00567905 1.04924321 + vae.decoder 0.00019376 0.07859966 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 1.75069060 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 105032 +BPFP 0.3716 bits/point +EBPFP 0.7433 equivalent bits/point +MSE 1.750691 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.153s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7507 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,872B, BPFP=0.6591 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,448B, BPFP=0.6857 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,684B, BPFP=0.6768 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,020B, BPFP=0.2292 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,024B, BPFP=0.2292 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,192B, BPFP=0.2195 +⌛️ [2/4] FRONTEND: Frontend time: 2.160s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.81208944 + text_encoder-item0.clip_prompt_embeds 0.00024281 23.87982151 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.80562038 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.11302992 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00303450 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.02387581 1.74501491 + vae.encoder_f1 0.02387858 1.74432397 + vae.decoder 0.00018648 0.03915877 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 2.06869547 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 106996 +BPFP 0.3786 bits/point +EBPFP 0.7572 equivalent bits/point +MSE 2.068695 +---------------------- -------------------------------------------------------- +Time: 3.770s Load: 0.008s, Pack+Encode: 2.160s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0687 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,852B, BPFP=0.6564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,288B, BPFP=0.6727 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,840B, BPFP=0.6047 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,268B, BPFP=0.3703 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,256B, BPFP=0.3701 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,960B, BPFP=0.1514 +⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.85396155 + text_encoder-item0.clip_prompt_embeds 0.00022399 23.71671021 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.81667871 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.10594823 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00248443 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.01169517 2.66622782 + vae.encoder_f1 0.01169969 2.67071462 + vae.decoder 0.00021186 0.03367514 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 2.49185840 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 120184 +BPFP 0.4252 bits/point +EBPFP 0.8505 equivalent bits/point +MSE 2.491858 +---------------------- -------------------------------------------------------- +Time: 3.776s Load: 0.010s, Pack+Encode: 2.163s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4919 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,872B, BPFP=0.6591 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 796B, BPFP=4.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,300B, BPFP=0.6737 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,644B, BPFP=0.7266 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,592B, BPFP=0.2837 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,588B, BPFP=0.2836 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,780B, BPFP=0.2985 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.83546487 + text_encoder-item0.clip_prompt_embeds 0.00022123 23.86700360 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.83138943 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.11580614 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00286291 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.32749966 4.39152384 + vae.encoder_f1 0.32750070 4.38916254 + vae.decoder 0.00039956 0.05304505 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 3.29706897 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118496 +BPFP 0.4193 bits/point +EBPFP 0.8385 equivalent bits/point +MSE 3.297069 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.009s, Pack+Encode: 2.144s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2971 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,136B, BPFP=0.6948 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 776B, BPFP=4.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,976B, BPFP=0.7286 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,676B, BPFP=0.5752 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,372B, BPFP=0.2346 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,372B, BPFP=0.2346 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,288B, BPFP=0.2834 +⌛️ [2/4] FRONTEND: Frontend time: 2.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.585s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.81364536 + text_encoder-item0.clip_prompt_embeds 0.00024675 23.79457099 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.80297699 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.11245878 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00244412 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00566967 1.29679966 + vae.encoder_f1 0.00567867 1.29620922 + vae.decoder 0.00017839 0.05451323 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 1.86029313 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 106508 +BPFP 0.3769 bits/point +EBPFP 0.7537 equivalent bits/point +MSE 1.860293 +---------------------- -------------------------------------------------------- +Time: 3.726s Load: 0.008s, Pack+Encode: 2.132s, Decode+Unpack: 1.585s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8603 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 572B, BPFP=5.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,244B, BPFP=0.5741 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 836B, BPFP=5.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,684B, BPFP=0.6237 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,952B, BPFP=0.6075 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,556B, BPFP=0.1763 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,556B, BPFP=0.1763 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,236B, BPFP=0.3734 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.77733270 + text_encoder-item0.clip_prompt_embeds 0.00022364 23.88428791 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.94515018 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.10688800 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00230403 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00580750 1.19199264 + vae.encoder_f1 0.00580664 1.19193065 + vae.decoder 0.00018044 0.08077567 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 1.81700674 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 101016 +BPFP 0.3574 bits/point +EBPFP 0.7148 equivalent bits/point +MSE 1.817007 +---------------------- -------------------------------------------------------- +Time: 3.726s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8170 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,644B, BPFP=0.6282 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,132B, BPFP=0.6601 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,804B, BPFP=0.5784 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,540B, BPFP=0.2524 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,536B, BPFP=0.2523 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,708B, BPFP=0.1437 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.82641276 + text_encoder-item0.clip_prompt_embeds 0.00030118 23.85233191 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.76949177 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.10923803 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00269960 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.03869025 2.56461692 + vae.encoder_f1 0.03869358 2.55490947 + vae.decoder 0.00021614 0.04531607 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 2.44647727 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 103104 +BPFP 0.3648 bits/point +EBPFP 0.7296 equivalent bits/point +MSE 2.446477 +---------------------- -------------------------------------------------------- +Time: 3.736s Load: 0.009s, Pack+Encode: 2.140s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4465 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 4,432B, BPFP=0.5996 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 828B, BPFP=5.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 7,936B, BPFP=0.6442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 22,556B, BPFP=0.5721 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,676B, BPFP=0.3613 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,672B, BPFP=0.3612 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,628B, BPFP=0.1412 +⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.86203583 + text_encoder-item0.clip_prompt_embeds 0.00023260 23.83115826 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.88921719 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.11090948 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00270570 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00839879 2.36535692 + vae.encoder_f1 0.00840224 2.36318493 + vae.decoder 0.00019463 0.04074376 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 2.35488387 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 116640 +BPFP 0.4127 bits/point +EBPFP 0.8254 equivalent bits/point +MSE 2.354884 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.009s, Pack+Encode: 2.135s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3549 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,612B, BPFP=0.7592 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 860B, BPFP=5.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,140B, BPFP=0.8231 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,124B, BPFP=0.6373 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,756B, BPFP=0.3320 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,764B, BPFP=0.3321 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,756B, BPFP=0.1757 +⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.85033369 + text_encoder-item0.clip_prompt_embeds 0.00023544 23.77243769 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.84690228 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.11714505 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00324178 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.01160815 2.37187171 + vae.encoder_f1 0.01161249 2.37318802 + vae.decoder 0.00021720 0.03907328 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 2.35730326 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 119948 +BPFP 0.4244 bits/point +EBPFP 0.8488 equivalent bits/point +MSE 2.357303 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.008s, Pack+Encode: 2.136s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3573 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,072B, BPFP=0.6861 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,652B, BPFP=0.7023 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,084B, BPFP=0.6109 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,284B, BPFP=0.2027 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,288B, BPFP=0.2028 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,364B, BPFP=0.1637 +⌛️ [2/4] FRONTEND: Frontend time: 2.157s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.80088584 + text_encoder-item0.clip_prompt_embeds 0.00022923 23.88239609 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.85791693 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.10917158 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00255345 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.02989292 2.56563020 + vae.encoder_f1 0.02989391 2.56470323 + vae.decoder 0.00034944 0.03552894 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 2.44865294 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 99480 +BPFP 0.3520 bits/point +EBPFP 0.7040 equivalent bits/point +MSE 2.448653 +---------------------- -------------------------------------------------------- +Time: 3.762s Load: 0.007s, Pack+Encode: 2.157s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4487 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,232B, BPFP=0.7078 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 828B, BPFP=5.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 8,748B, BPFP=0.7101 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,584B, BPFP=0.6236 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,824B, BPFP=0.3330 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,824B, BPFP=0.3330 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,740B, BPFP=0.2057 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.88776310 + text_encoder-item0.clip_prompt_embeds 0.00024627 23.81219773 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.89471006 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.10257251 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00272501 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 + vae.encoder_f0 0.00613025 1.69518030 + vae.encoder_f1 0.00613536 1.69508851 + vae.decoder 0.00018697 0.05500416 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 2.04536827 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118700 +BPFP 0.4200 bits/point +EBPFP 0.8400 equivalent bits/point +MSE 2.045368 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.009s, Pack+Encode: 2.141s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0454 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.3901 bits/point +Avg EBPFP 0.7801 equivalent bits/point +Avg MSE 2.246105 +Avg Time 3.761s +------------------------ ---------------------------- diff --git a/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log b/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..5f2558977e50d2d719ddb694f7a24e4d838d28b6 --- /dev/null +++ b/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log @@ -0,0 +1,21862 @@ +Experiment: dtufc_hyperprior-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: sd35 + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 559 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,772B, BPFP=0.9161 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,768B, BPFP=0.8740 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 31,568B, BPFP=0.8007 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,704B, BPFP=0.3159 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,704B, BPFP=0.3159 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,464B, BPFP=0.2583 +⌛️ [2/4] FRONTEND: Frontend time: 0.692s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.95494636 + text_encoder-item0.clip_prompt_embeds 0.00025464 35.16017739 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.85673828 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.13009319 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00365759 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00635250 2.62802196 + vae.encoder_f1 0.00635834 2.62814617 + vae.decoder 0.00019940 0.05525560 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 2.79863094 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 132388 +BPFP 0.4684 bits/point +EBPFP 0.9368 equivalent bits/point +MSE 2.798631 +---------------------- -------------------------------------------------------- +Time: 1.210s Load: 0.008s, Pack+Encode: 0.692s, Decode+Unpack: 0.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7986 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,864B, BPFP=0.9286 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,164B, BPFP=0.9062 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,624B, BPFP=0.7514 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,936B, BPFP=0.2432 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,940B, BPFP=0.2432 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,660B, BPFP=0.2032 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.92112398 + text_encoder-item0.clip_prompt_embeds 0.00022609 108.26190476 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.91517315 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.18779333 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00359077 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.01130640 3.32165742 + vae.encoder_f1 0.01130902 3.32108355 + vae.decoder 0.00020860 0.05133892 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 5.03419621 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 119584 +BPFP 0.4231 bits/point +EBPFP 0.8462 equivalent bits/point +MSE 5.034196 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.0342 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,420B, BPFP=0.8685 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,216B, BPFP=7.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,920B, BPFP=0.8864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,776B, BPFP=0.6792 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,856B, BPFP=0.1351 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,856B, BPFP=0.1351 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,060B, BPFP=0.1544 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.95281442 + text_encoder-item0.clip_prompt_embeds 0.00022402 47.98036729 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 1.11377983 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.12499061 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00295826 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 1.19630027 6.77417183 + vae.encoder_f1 1.19630098 6.77337694 + vae.decoder 0.00023596 0.04179461 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 5.05484472 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 100300 +BPFP 0.3549 bits/point +EBPFP 0.7098 equivalent bits/point +MSE 5.054845 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.007s, Pack+Encode: 0.292s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.0548 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,756B, BPFP=0.9140 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,216B, BPFP=7.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,572B, BPFP=0.9393 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 30,700B, BPFP=0.7787 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,276B, BPFP=0.2484 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,276B, BPFP=0.2484 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,496B, BPFP=0.2898 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.95865774 + text_encoder-item0.clip_prompt_embeds 0.00030342 23.95113171 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.87567692 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.12840412 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00373103 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00586287 1.98412931 + vae.encoder_f1 0.00587438 1.98380661 + vae.decoder 0.00017677 0.08426298 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 2.21005083 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 124484 +BPFP 0.4405 bits/point +EBPFP 0.8809 equivalent bits/point +MSE 2.210051 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2101 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,888B, BPFP=0.7965 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,472B, BPFP=0.9312 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,820B, BPFP=0.7057 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,292B, BPFP=0.2181 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,292B, BPFP=0.2181 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,272B, BPFP=0.1914 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.90704974 + text_encoder-item0.clip_prompt_embeds 0.00024120 24.47940764 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.97753305 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.11967713 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00291543 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00779453 2.41266918 + vae.encoder_f1 0.00779802 2.41255021 + vae.decoder 0.00023829 0.04979095 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 2.41820738 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 113444 +BPFP 0.4014 bits/point +EBPFP 0.8028 equivalent bits/point +MSE 2.418207 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4182 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,164B, BPFP=0.8339 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,480B, BPFP=0.9318 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,436B, BPFP=0.7213 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,532B, BPFP=0.3286 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,536B, BPFP=0.3286 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,976B, BPFP=0.2129 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.94154310 + text_encoder-item0.clip_prompt_embeds 0.00025651 23.90523962 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.95418129 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.13101699 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00344603 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00655775 2.89312196 + vae.encoder_f1 0.00656268 2.89120626 + vae.decoder 0.00020283 0.05328232 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 2.62656375 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 129520 +BPFP 0.4583 bits/point +EBPFP 0.9166 equivalent bits/point +MSE 2.626564 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6266 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,456B, BPFP=0.7381 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,932B, BPFP=0.8062 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,936B, BPFP=0.6071 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,812B, BPFP=0.2870 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,816B, BPFP=0.2871 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,900B, BPFP=0.2411 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.94820023 + text_encoder-item0.clip_prompt_embeds 0.00022242 23.93856323 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.94795275 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.11797708 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00337748 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00593415 2.23211765 + vae.encoder_f1 0.00594307 2.23192954 + vae.decoder 0.00018992 0.07523341 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 2.32324901 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118256 +BPFP 0.4184 bits/point +EBPFP 0.8368 equivalent bits/point +MSE 2.323249 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3232 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,676B, BPFP=0.9031 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,140B, BPFP=0.9042 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,472B, BPFP=0.6968 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,964B, BPFP=0.2894 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,964B, BPFP=0.2894 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,592B, BPFP=0.2317 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.93140324 + text_encoder-item0.clip_prompt_embeds 0.00022110 132.52475649 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.89787722 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.14335624 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00320222 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00641770 2.36619711 + vae.encoder_f1 0.00642053 2.36633277 + vae.decoder 0.00017498 0.04471630 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 5.22307554 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 124204 +BPFP 0.4395 bits/point +EBPFP 0.8789 equivalent bits/point +MSE 5.223076 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.2231 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,440B, BPFP=0.7359 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,112B, BPFP=0.8208 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,452B, BPFP=0.6963 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,972B, BPFP=0.2132 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,972B, BPFP=0.2132 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,928B, BPFP=0.2419 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.90416344 + text_encoder-item0.clip_prompt_embeds 0.00021654 36.00623985 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.98446150 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.12336559 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00362558 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00577698 1.94225919 + vae.encoder_f1 0.00578348 1.94221735 + vae.decoder 0.00017559 0.06701561 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 2.50380700 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 112276 +BPFP 0.3973 bits/point +EBPFP 0.7945 equivalent bits/point +MSE 2.503807 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5038 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,268B, BPFP=0.8479 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,504B, BPFP=0.8526 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,648B, BPFP=0.7267 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,116B, BPFP=0.2612 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,116B, BPFP=0.2612 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,076B, BPFP=0.1854 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.96714354 + text_encoder-item0.clip_prompt_embeds 0.00022160 24.23977780 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.83238535 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.12087520 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00338200 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00668450 2.29490089 + vae.encoder_f1 0.00668875 2.29468369 + vae.decoder 0.00023059 0.05260476 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 2.35768172 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 119120 +BPFP 0.4215 bits/point +EBPFP 0.8430 equivalent bits/point +MSE 2.357682 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3577 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,696B, BPFP=0.9058 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,936B, BPFP=0.8877 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,528B, BPFP=0.7490 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,540B, BPFP=0.2524 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,540B, BPFP=0.2524 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,792B, BPFP=0.1462 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.92981633 + text_encoder-item0.clip_prompt_embeds 0.00023190 23.94112723 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.79946666 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.13031174 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00381124 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.04018118 3.47797251 + vae.encoder_f1 0.04018488 3.47876120 + vae.decoder 0.00016201 0.03279821 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 2.89691819 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118428 +BPFP 0.4190 bits/point +EBPFP 0.8381 equivalent bits/point +MSE 2.896918 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8969 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,944B, BPFP=0.8041 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,968B, BPFP=0.8903 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,700B, BPFP=0.6773 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,612B, BPFP=0.2840 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,616B, BPFP=0.2841 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,128B, BPFP=0.2480 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.96030140 + text_encoder-item0.clip_prompt_embeds 0.00023140 24.21897195 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.87784100 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.11179552 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00377791 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.04874706 3.34490633 + vae.encoder_f1 0.04875064 3.34430313 + vae.decoder 0.00019641 0.04781711 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 2.84313483 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 122380 +BPFP 0.4330 bits/point +EBPFP 0.8660 equivalent bits/point +MSE 2.843135 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8431 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,484B, BPFP=0.8772 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,016B, BPFP=0.8942 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,644B, BPFP=0.6505 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,520B, BPFP=0.3436 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,524B, BPFP=0.3437 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,384B, BPFP=0.1338 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.91339238 + text_encoder-item0.clip_prompt_embeds 0.00030893 23.94655540 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.92015295 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.12518289 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00317264 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.01360236 3.38075852 + vae.encoder_f1 0.01360807 3.38128018 + vae.decoder 0.00023006 0.03794406 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 2.85226021 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 125968 +BPFP 0.4457 bits/point +EBPFP 0.8914 equivalent bits/point +MSE 2.852260 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8523 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,236B, BPFP=0.9789 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,924B, BPFP=0.8867 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 30,672B, BPFP=0.7780 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,448B, BPFP=0.1289 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,448B, BPFP=0.1289 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 2,864B, BPFP=0.0874 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.93097440 + text_encoder-item0.clip_prompt_embeds 0.00024198 23.94506942 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.91722708 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.12656511 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00341539 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 1.67190456 6.81759834 + vae.encoder_f1 1.67190480 6.81456280 + vae.decoder 0.00017417 0.02075510 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 4.44339872 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 102004 +BPFP 0.3609 bits/point +EBPFP 0.7218 equivalent bits/point +MSE 4.443399 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.007s, Pack+Encode: 0.292s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4434 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,676B, BPFP=0.9031 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,260B, BPFP=0.9140 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 31,412B, BPFP=0.7968 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,964B, BPFP=0.3351 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,964B, BPFP=0.3351 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,540B, BPFP=0.2301 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.90416606 + text_encoder-item0.clip_prompt_embeds 0.00025129 23.93954824 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.90859032 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.12572472 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00359675 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00621760 2.49274087 + vae.encoder_f1 0.00622505 2.49173641 + vae.decoder 0.00025114 0.06197819 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 2.44274845 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 134216 +BPFP 0.4749 bits/point +EBPFP 0.9498 equivalent bits/point +MSE 2.442748 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4427 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,184B, BPFP=0.8366 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,020B, BPFP=0.8945 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,292B, BPFP=0.6669 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,844B, BPFP=0.3638 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,844B, BPFP=0.3638 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,520B, BPFP=0.2295 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.97099225 + text_encoder-item0.clip_prompt_embeds 0.00020838 48.61449455 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.92767897 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.16369402 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00270878 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00675961 3.14311695 + vae.encoder_f1 0.00676652 3.14347458 + vae.decoder 0.00021373 0.06481232 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 3.39195190 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 132100 +BPFP 0.4674 bits/point +EBPFP 0.9348 equivalent bits/point +MSE 3.391952 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3920 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,904B, BPFP=0.9340 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,292B, BPFP=0.9166 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 30,416B, BPFP=0.7715 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 10,704B, BPFP=0.1633 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 10,704B, BPFP=0.1633 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,048B, BPFP=0.3677 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.90248601 + text_encoder-item0.clip_prompt_embeds 0.00021387 107.03816626 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.88290014 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.13265011 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00363773 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00596338 1.31515718 + vae.encoder_f1 0.00596322 1.31507826 + vae.decoder 0.00018207 0.10055342 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 4.07503760 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 115464 +BPFP 0.4085 bits/point +EBPFP 0.8171 equivalent bits/point +MSE 4.075038 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0750 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,460B, BPFP=0.8739 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,056B, BPFP=0.8974 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,628B, BPFP=0.6501 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 8,580B, BPFP=0.1309 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 8,580B, BPFP=0.1309 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,348B, BPFP=0.2548 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.87907076 + text_encoder-item0.clip_prompt_embeds 0.00022138 35.96740564 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.89552355 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.17737611 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00283273 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00552804 0.96921045 + vae.encoder_f1 0.00552758 0.96921027 + vae.decoder 0.00018040 0.07720087 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 2.05489781 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 102048 +BPFP 0.3611 bits/point +EBPFP 0.7221 equivalent bits/point +MSE 2.054898 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0549 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,696B, BPFP=0.7706 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,484B, BPFP=0.9321 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,244B, BPFP=0.6657 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,288B, BPFP=0.1875 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,292B, BPFP=0.1876 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,792B, BPFP=0.1768 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.96792213 + text_encoder-item0.clip_prompt_embeds 0.00024507 23.94371237 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.94905024 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.12641346 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00307950 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00721525 1.92423761 + vae.encoder_f1 0.00721777 1.92418337 + vae.decoder 0.00018707 0.04186224 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 2.17709416 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 107184 +BPFP 0.3792 bits/point +EBPFP 0.7585 equivalent bits/point +MSE 2.177094 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1771 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,160B, BPFP=0.6981 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,776B, BPFP=0.7935 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,832B, BPFP=0.6045 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,864B, BPFP=0.2878 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,864B, BPFP=0.2878 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,576B, BPFP=0.2312 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.92997726 + text_encoder-item0.clip_prompt_embeds 0.00046272 131.90384876 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.98846159 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.12330389 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00301164 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.01999603 3.47811007 + vae.encoder_f1 0.01999529 3.47828436 + vae.decoder 0.00024882 0.06096582 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 5.72354861 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 117456 +BPFP 0.4156 bits/point +EBPFP 0.8312 equivalent bits/point +MSE 5.723549 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.7235 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,608B, BPFP=0.7587 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,100B, BPFP=0.9010 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,740B, BPFP=0.6022 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,680B, BPFP=0.3003 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,680B, BPFP=0.3003 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,528B, BPFP=0.1687 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.91544143 + text_encoder-item0.clip_prompt_embeds 0.00020334 35.96247210 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.86179924 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.12618976 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00312333 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.01341345 3.12336135 + vae.encoder_f1 0.01341645 3.12268400 + vae.decoder 0.00018350 0.03317202 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 3.04633597 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118736 +BPFP 0.4201 bits/point +EBPFP 0.8402 equivalent bits/point +MSE 3.046336 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0463 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,312B, BPFP=0.8539 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,428B, BPFP=0.9276 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,148B, BPFP=0.7140 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,372B, BPFP=0.2803 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,372B, BPFP=0.2803 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,440B, BPFP=0.2881 +⌛️ [2/4] FRONTEND: Frontend time: 0.305s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.92774375 + text_encoder-item0.clip_prompt_embeds 0.00022316 144.03671199 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.92485466 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.15045479 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00335829 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00606298 2.17639184 + vae.encoder_f1 0.00607096 2.17661667 + vae.decoder 0.00023408 0.07605585 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 5.44014334 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 125468 +BPFP 0.4439 bits/point +EBPFP 0.8879 equivalent bits/point +MSE 5.440143 +---------------------- -------------------------------------------------------- +Time: 0.764s Load: 0.008s, Pack+Encode: 0.305s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.4401 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,784B, BPFP=0.7825 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,648B, BPFP=0.7831 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,864B, BPFP=0.7321 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,928B, BPFP=0.2736 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,928B, BPFP=0.2736 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,964B, BPFP=0.2736 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.91855677 + text_encoder-item0.clip_prompt_embeds 0.00023597 23.94418797 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.92909241 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.11034792 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00300961 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00653100 2.34386539 + vae.encoder_f1 0.00653745 2.34383154 + vae.decoder 0.00020026 0.06500594 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 2.37366651 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 122516 +BPFP 0.4335 bits/point +EBPFP 0.8670 equivalent bits/point +MSE 2.373667 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3737 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,704B, BPFP=0.9069 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,068B, BPFP=0.8984 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,896B, BPFP=0.6569 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,516B, BPFP=0.2825 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,512B, BPFP=0.2825 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,732B, BPFP=0.1749 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.90260911 + text_encoder-item0.clip_prompt_embeds 0.00022433 23.94183957 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.94458714 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.12464044 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00310253 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00869686 3.59393263 + vae.encoder_f1 0.00870063 3.59585857 + vae.decoder 0.00021246 0.04679004 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 2.95232822 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 119824 +BPFP 0.4240 bits/point +EBPFP 0.8479 equivalent bits/point +MSE 2.952328 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9523 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,716B, BPFP=0.9085 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,192B, BPFP=0.9084 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,404B, BPFP=0.7458 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,080B, BPFP=0.3217 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,080B, BPFP=0.3217 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,572B, BPFP=0.2006 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.94367552 + text_encoder-item0.clip_prompt_embeds 0.00022433 34.91684000 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.89953356 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.15691746 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00357857 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00626512 2.94792724 + vae.encoder_f1 0.00626949 2.94782734 + vae.decoder 0.00018936 0.04929534 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 2.94106402 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 129448 +BPFP 0.4580 bits/point +EBPFP 0.9160 equivalent bits/point +MSE 2.941064 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9411 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,916B, BPFP=0.9356 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,604B, BPFP=0.9419 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,036B, BPFP=0.7365 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,564B, BPFP=0.2375 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,564B, BPFP=0.2375 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,356B, BPFP=0.1635 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.98434607 + text_encoder-item0.clip_prompt_embeds 0.00026137 60.59005513 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.84837189 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.13495667 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00326189 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.35915655 5.12527657 + vae.encoder_f1 0.35915723 5.12704611 + vae.decoder 0.00024181 0.03853196 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 4.62049830 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 117436 +BPFP 0.4155 bits/point +EBPFP 0.8310 equivalent bits/point +MSE 4.620498 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.6205 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,252B, BPFP=0.7105 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,808B, BPFP=0.7961 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,972B, BPFP=0.6081 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,840B, BPFP=0.1196 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,840B, BPFP=0.1196 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,324B, BPFP=0.1320 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.95771750 + text_encoder-item0.clip_prompt_embeds 0.00021656 193.98380005 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.94013290 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.11295096 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00303997 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.29031765 3.89296794 + vae.encoder_f1 0.29031771 3.89294100 + vae.decoder 0.00019965 0.05006742 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 7.53786531 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 92452 +BPFP 0.3271 bits/point +EBPFP 0.6542 equivalent bits/point +MSE 7.537865 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.5379 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,056B, BPFP=0.6840 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,592B, BPFP=0.8597 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,504B, BPFP=0.5962 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,124B, BPFP=0.2155 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,128B, BPFP=0.2156 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,764B, BPFP=0.3285 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.96516999 + text_encoder-item0.clip_prompt_embeds 0.00025451 36.83176069 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.87337875 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.11525207 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00289896 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00595764 1.53567481 + vae.encoder_f1 0.00596395 1.53542089 + vae.decoder 0.00019845 0.08592859 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 2.33848397 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 111556 +BPFP 0.3947 bits/point +EBPFP 0.7894 equivalent bits/point +MSE 2.338484 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3385 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,360B, BPFP=0.8604 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,872B, BPFP=0.8825 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,800B, BPFP=0.7559 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,952B, BPFP=0.1824 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,952B, BPFP=0.1824 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,896B, BPFP=0.1494 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.92405295 + text_encoder-item0.clip_prompt_embeds 0.00026157 23.94255191 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.91029253 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.13882221 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00367807 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.40456498 6.01515341 + vae.encoder_f1 0.40456539 6.01513529 + vae.decoder 0.00020503 0.03923426 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 4.07459138 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 109224 +BPFP 0.3865 bits/point +EBPFP 0.7729 equivalent bits/point +MSE 4.074591 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0746 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,008B, BPFP=0.8128 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,504B, BPFP=0.8526 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,968B, BPFP=0.6333 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,356B, BPFP=0.2953 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,356B, BPFP=0.2953 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,832B, BPFP=0.2695 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.91871460 + text_encoder-item0.clip_prompt_embeds 0.00027179 23.94517933 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.93989544 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.12223185 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00254879 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00673531 3.22262645 + vae.encoder_f1 0.00673732 3.22259521 + vae.decoder 0.00020129 0.07213721 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 2.78252114 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 122416 +BPFP 0.4331 bits/point +EBPFP 0.8663 equivalent bits/point +MSE 2.782521 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7825 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,572B, BPFP=0.8891 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,000B, BPFP=0.8929 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 30,152B, BPFP=0.7648 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,756B, BPFP=0.2557 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,756B, BPFP=0.2557 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,916B, BPFP=0.1500 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.93672069 + text_encoder-item0.clip_prompt_embeds 0.00023057 23.92751243 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.94562416 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.13309284 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00336660 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00881784 3.07203364 + vae.encoder_f1 0.00882136 3.07392502 + vae.decoder 0.00017598 0.03536221 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 2.70899786 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 119544 +BPFP 0.4230 bits/point +EBPFP 0.8460 equivalent bits/point +MSE 2.708998 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7090 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,428B, BPFP=0.8696 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,268B, BPFP=0.9146 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,604B, BPFP=0.7002 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,000B, BPFP=0.2136 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,000B, BPFP=0.2136 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,228B, BPFP=0.2511 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.93624973 + text_encoder-item0.clip_prompt_embeds 0.00025208 34.95843057 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.90315208 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.13290990 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00324598 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00582247 1.67626643 + vae.encoder_f1 0.00582996 1.67604637 + vae.decoder 0.00016099 0.07558111 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 2.35432245 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114920 +BPFP 0.4066 bits/point +EBPFP 0.8132 equivalent bits/point +MSE 2.354322 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3543 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,204B, BPFP=0.8393 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,184B, BPFP=7.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,236B, BPFP=0.9120 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,156B, BPFP=0.6635 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,128B, BPFP=0.3224 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,128B, BPFP=0.3224 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,992B, BPFP=0.2134 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.93581033 + text_encoder-item0.clip_prompt_embeds 0.00020809 36.01152006 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.92333622 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.13325733 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00319981 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00602745 2.87793422 + vae.encoder_f1 0.00603159 2.87791896 + vae.decoder 0.00017526 0.05981690 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 2.93740083 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 126236 +BPFP 0.4467 bits/point +EBPFP 0.8933 equivalent bits/point +MSE 2.937401 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9374 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,564B, BPFP=0.7527 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,632B, BPFP=0.8630 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,700B, BPFP=0.6519 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,640B, BPFP=0.3302 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,636B, BPFP=0.3301 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,584B, BPFP=0.2620 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.90850528 + text_encoder-item0.clip_prompt_embeds 0.00020908 24.22980291 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.91163120 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.11880111 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00244882 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00634616 2.90769339 + vae.encoder_f1 0.00635208 2.90866613 + vae.decoder 0.00022721 0.06450719 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 2.64307473 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 127164 +BPFP 0.4499 bits/point +EBPFP 0.8999 equivalent bits/point +MSE 2.643075 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6431 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,732B, BPFP=0.7754 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,308B, BPFP=0.8367 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,976B, BPFP=0.6335 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,628B, BPFP=0.1774 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,624B, BPFP=0.1774 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,276B, BPFP=0.1305 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.95460097 + text_encoder-item0.clip_prompt_embeds 0.00022947 84.56356534 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.99602556 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.11531809 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00249646 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.05448642 2.74767041 + vae.encoder_f1 0.05448771 2.74699283 + vae.decoder 0.00017748 0.03015359 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 4.14243671 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 101948 +BPFP 0.3607 bits/point +EBPFP 0.7214 equivalent bits/point +MSE 4.142437 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1424 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,348B, BPFP=0.8588 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,172B, BPFP=0.9068 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,852B, BPFP=0.7065 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,676B, BPFP=0.2392 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,676B, BPFP=0.2392 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,180B, BPFP=0.1581 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.93440914 + text_encoder-item0.clip_prompt_embeds 0.00020169 23.95497455 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.89337587 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.13715159 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00369594 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.06876971 3.81661367 + vae.encoder_f1 0.06877109 3.81584835 + vae.decoder 0.00023999 0.02866444 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 3.05382852 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 115308 +BPFP 0.4080 bits/point +EBPFP 0.8160 equivalent bits/point +MSE 3.053829 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0538 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,540B, BPFP=0.8847 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,408B, BPFP=0.9260 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,136B, BPFP=0.7137 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,432B, BPFP=0.2050 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,432B, BPFP=0.2050 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,980B, BPFP=0.2740 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.92201018 + text_encoder-item0.clip_prompt_embeds 0.00025253 23.93719984 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.96261625 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.12794205 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00280123 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00595097 1.49915934 + vae.encoder_f1 0.00595882 1.49926889 + vae.decoder 0.00020134 0.08623081 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 1.98498814 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 115328 +BPFP 0.4081 bits/point +EBPFP 0.8161 equivalent bits/point +MSE 1.984988 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9850 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,424B, BPFP=0.8690 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,564B, BPFP=0.9386 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,828B, BPFP=0.7059 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,848B, BPFP=0.1960 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,848B, BPFP=0.1960 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,500B, BPFP=0.2899 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.94159349 + text_encoder-item0.clip_prompt_embeds 0.00022201 23.93505859 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.94038029 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.14154603 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00323732 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00831743 2.48620129 + vae.encoder_f1 0.00831926 2.48616362 + vae.decoder 0.00028593 0.06129293 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 2.44041315 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114420 +BPFP 0.4048 bits/point +EBPFP 0.8097 equivalent bits/point +MSE 2.440413 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4404 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,784B, BPFP=0.9177 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,992B, BPFP=0.8922 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,908B, BPFP=0.7586 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,448B, BPFP=0.3120 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,452B, BPFP=0.3121 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,424B, BPFP=0.2266 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.93228912 + text_encoder-item0.clip_prompt_embeds 0.00026808 23.93481974 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.96259098 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.14612143 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00398927 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00606586 3.10035968 + vae.encoder_f1 0.00607066 3.09903717 + vae.decoder 0.00019664 0.05825450 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 2.72489752 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 129408 +BPFP 0.4579 bits/point +EBPFP 0.9158 equivalent bits/point +MSE 2.724898 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7249 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,620B, BPFP=0.8956 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,776B, BPFP=0.8747 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,292B, BPFP=0.7430 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,024B, BPFP=0.2445 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,024B, BPFP=0.2445 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,568B, BPFP=0.2004 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.94207589 + text_encoder-item0.clip_prompt_embeds 0.00023198 34.93996254 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.98193111 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.14449975 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00277646 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.05216765 3.80797935 + vae.encoder_f1 0.05216896 3.80751586 + vae.decoder 0.00017960 0.04968261 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 3.33988703 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 118692 +BPFP 0.4200 bits/point +EBPFP 0.8399 equivalent bits/point +MSE 3.339887 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3399 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,000B, BPFP=0.8117 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,056B, BPFP=0.8974 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,324B, BPFP=0.6423 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,488B, BPFP=0.3126 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,488B, BPFP=0.3126 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,824B, BPFP=0.2083 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.94063735 + text_encoder-item0.clip_prompt_embeds 0.00023125 23.95665077 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.97316809 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.16126856 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00284531 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00620361 2.58321953 + vae.encoder_f1 0.00620966 2.58307409 + vae.decoder 0.00020748 0.05751245 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 2.48633184 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 123572 +BPFP 0.4372 bits/point +EBPFP 0.8745 equivalent bits/point +MSE 2.486332 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4863 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 748B, BPFP=7.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,944B, BPFP=0.8041 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,184B, BPFP=7.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,788B, BPFP=0.8756 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,728B, BPFP=0.6780 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,380B, BPFP=0.2805 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,380B, BPFP=0.2805 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,364B, BPFP=0.2552 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.91874607 + text_encoder-item0.clip_prompt_embeds 0.00023066 23.95234713 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.85953379 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.13225843 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00375941 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.03159856 2.55363798 + vae.encoder_f1 0.03160188 2.55290556 + vae.decoder 0.00018417 0.05672223 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 2.47106372 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 121980 +BPFP 0.4316 bits/point +EBPFP 0.8632 equivalent bits/point +MSE 2.471064 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4711 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,364B, BPFP=0.8609 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,832B, BPFP=0.8792 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,536B, BPFP=0.6985 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,176B, BPFP=0.3536 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,172B, BPFP=0.3536 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,484B, BPFP=0.1979 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.91375510 + text_encoder-item0.clip_prompt_embeds 0.00024948 23.94296199 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.86925554 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.12651515 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00391921 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.03490865 4.28346348 + vae.encoder_f1 0.03491008 4.28428364 + vae.decoder 0.00028462 0.05988157 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 3.27355822 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 130960 +BPFP 0.4634 bits/point +EBPFP 0.9267 equivalent bits/point +MSE 3.273558 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2736 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,768B, BPFP=0.7803 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,008B, BPFP=0.8935 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,536B, BPFP=0.7492 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,800B, BPFP=0.1495 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,800B, BPFP=0.1495 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,448B, BPFP=0.2578 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.92312511 + text_encoder-item0.clip_prompt_embeds 0.00021560 23.92873419 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.85583429 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.12873979 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00350815 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00544735 1.08977914 + vae.encoder_f1 0.00544843 1.08977556 + vae.decoder 0.00018632 0.07804158 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 1.79400684 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 107764 +BPFP 0.3813 bits/point +EBPFP 0.7626 equivalent bits/point +MSE 1.794007 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7940 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,988B, BPFP=0.8101 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,256B, BPFP=0.9136 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,300B, BPFP=0.6417 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,316B, BPFP=0.2947 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,316B, BPFP=0.2947 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,424B, BPFP=0.2571 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.91010276 + text_encoder-item0.clip_prompt_embeds 0.00022698 143.97444873 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.96758766 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.12269690 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00311084 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00630479 2.25577331 + vae.encoder_f1 0.00631430 2.25579500 + vae.decoder 0.00018596 0.05676587 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 5.47181949 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 122996 +BPFP 0.4352 bits/point +EBPFP 0.8704 equivalent bits/point +MSE 5.471819 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.4718 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,008B, BPFP=0.8128 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,952B, BPFP=0.8890 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,284B, BPFP=0.5906 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,160B, BPFP=0.2771 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,160B, BPFP=0.2771 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,160B, BPFP=0.2795 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.98713930 + text_encoder-item0.clip_prompt_embeds 0.00024643 23.96090157 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.99402981 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.12742973 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00265886 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00612578 2.08261251 + vae.encoder_f1 0.00613243 2.08300090 + vae.decoder 0.00018179 0.06517903 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 2.25381663 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 119116 +BPFP 0.4215 bits/point +EBPFP 0.8429 equivalent bits/point +MSE 2.253817 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2538 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,288B, BPFP=0.9859 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,508B, BPFP=0.9341 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 30,484B, BPFP=0.7732 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 6,772B, BPFP=0.1033 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 6,772B, BPFP=0.1033 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,864B, BPFP=0.3010 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.93068663 + text_encoder-item0.clip_prompt_embeds 0.00024049 34.89450588 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.95069761 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.13729683 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00370935 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00526071 0.45857000 + vae.encoder_f1 0.00526072 0.45856228 + vae.decoder 0.00016981 0.07236104 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 1.78787852 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 106072 +BPFP 0.3753 bits/point +EBPFP 0.7506 equivalent bits/point +MSE 1.787879 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7879 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,152B, BPFP=0.8323 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,776B, BPFP=0.8747 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,296B, BPFP=0.6670 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,276B, BPFP=0.2941 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,276B, BPFP=0.2941 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,260B, BPFP=0.2826 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.85254916 + text_encoder-item0.clip_prompt_embeds 0.00022843 59.98590114 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.90360165 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.11642714 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00298480 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00622977 2.36609244 + vae.encoder_f1 0.00623684 2.36605406 + vae.decoder 0.00019755 0.06656589 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 3.32704630 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 124444 +BPFP 0.4403 bits/point +EBPFP 0.8806 equivalent bits/point +MSE 3.327046 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3270 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,372B, BPFP=0.8620 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,068B, BPFP=0.8984 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,528B, BPFP=0.6983 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,468B, BPFP=0.2208 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,468B, BPFP=0.2208 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,464B, BPFP=0.1973 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.94531051 + text_encoder-item0.clip_prompt_embeds 0.00026004 24.03478423 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 1.01331501 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.11354867 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00314615 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00725303 1.98702812 + vae.encoder_f1 0.00725507 1.98784900 + vae.decoder 0.00017991 0.04503912 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 2.20864484 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 113760 +BPFP 0.4025 bits/point +EBPFP 0.8050 equivalent bits/point +MSE 2.208645 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2086 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,100B, BPFP=0.8252 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,704B, BPFP=0.8688 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,920B, BPFP=0.6828 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,100B, BPFP=0.2304 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,100B, BPFP=0.2304 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,268B, BPFP=0.1608 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.98833617 + text_encoder-item0.clip_prompt_embeds 0.00031748 23.93289198 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 1.00185385 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.12780132 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00308513 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.42111695 4.88432121 + vae.encoder_f1 0.42111716 4.88440990 + vae.decoder 0.00019827 0.03703041 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 3.54917459 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 112592 +BPFP 0.3984 bits/point +EBPFP 0.7968 equivalent bits/point +MSE 3.549175 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5492 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,512B, BPFP=0.8810 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,836B, BPFP=0.8795 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 30,632B, BPFP=0.7770 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,304B, BPFP=0.2640 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,304B, BPFP=0.2640 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,272B, BPFP=0.1914 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.90175700 + text_encoder-item0.clip_prompt_embeds 0.00024951 58.90313007 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.86725597 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.12510220 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00351340 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.10376993 5.10718250 + vae.encoder_f1 0.10377157 5.10824490 + vae.decoder 0.00019787 0.04295679 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 4.56792271 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 122264 +BPFP 0.4326 bits/point +EBPFP 0.8652 equivalent bits/point +MSE 4.567923 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.5679 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,128B, BPFP=0.8290 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,256B, BPFP=0.9136 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,084B, BPFP=0.7124 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,792B, BPFP=0.2715 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,788B, BPFP=0.2714 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,672B, BPFP=0.1731 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.93556595 + text_encoder-item0.clip_prompt_embeds 0.00022350 96.25196158 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.87969551 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.12995502 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00284950 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.01346414 3.28489757 + vae.encoder_f1 0.01346933 3.28296041 + vae.decoder 0.00019243 0.04102654 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 4.69887780 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 120116 +BPFP 0.4250 bits/point +EBPFP 0.8500 equivalent bits/point +MSE 4.698878 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.6989 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,888B, BPFP=0.7965 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,032B, BPFP=0.8143 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,168B, BPFP=0.7399 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,536B, BPFP=0.2371 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,536B, BPFP=0.2371 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,416B, BPFP=0.1653 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.460s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.91926599 + text_encoder-item0.clip_prompt_embeds 0.00024958 23.93213102 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 1.03085384 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.12885589 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00411961 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.11196710 4.44764996 + vae.encoder_f1 0.11196851 4.44640064 + vae.decoder 0.00023459 0.04331188 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 3.34724174 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114992 +BPFP 0.4069 bits/point +EBPFP 0.8137 equivalent bits/point +MSE 3.347242 +---------------------- -------------------------------------------------------- +Time: 0.766s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.460s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3472 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,328B, BPFP=0.8561 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,412B, BPFP=0.9263 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,956B, BPFP=0.6584 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,312B, BPFP=0.3099 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,316B, BPFP=0.3100 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,040B, BPFP=0.1843 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.94755220 + text_encoder-item0.clip_prompt_embeds 0.00025929 23.94684076 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.94049606 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.14851719 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00252624 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00675017 3.14154410 + vae.encoder_f1 0.00675421 3.14164591 + vae.decoder 0.00023635 0.05547103 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 2.74421254 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 123760 +BPFP 0.4379 bits/point +EBPFP 0.8758 equivalent bits/point +MSE 2.744213 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.010s, Pack+Encode: 0.294s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7442 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,768B, BPFP=0.7803 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,972B, BPFP=0.8906 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,880B, BPFP=0.7072 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,528B, BPFP=0.3743 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,528B, BPFP=0.3743 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,100B, BPFP=0.1862 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.89779695 + text_encoder-item0.clip_prompt_embeds 0.00064775 157.63193317 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.92495089 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.11822055 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00337716 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00728993 3.35865593 + vae.encoder_f1 0.00729572 3.35963917 + vae.decoder 0.00026488 0.05387092 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 6.34021233 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 133164 +BPFP 0.4712 bits/point +EBPFP 0.9423 equivalent bits/point +MSE 6.340212 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3402 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,012B, BPFP=0.8133 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,508B, BPFP=0.9341 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,260B, BPFP=0.7168 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,768B, BPFP=0.3016 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,768B, BPFP=0.3016 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,420B, BPFP=0.1959 +⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.96080939 + text_encoder-item0.clip_prompt_embeds 0.00023188 23.94390050 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.91358891 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.12621648 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00333941 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00613207 2.56686640 + vae.encoder_f1 0.00613899 2.56699157 + vae.decoder 0.00023812 0.05780491 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 2.47702503 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 125124 +BPFP 0.4427 bits/point +EBPFP 0.8854 equivalent bits/point +MSE 2.477025 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4770 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,360B, BPFP=0.8604 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,864B, BPFP=0.9630 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,048B, BPFP=0.7114 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,936B, BPFP=0.2737 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,936B, BPFP=0.2737 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,252B, BPFP=0.2213 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.90121889 + text_encoder-item0.clip_prompt_embeds 0.00023678 23.94834788 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.94244337 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.14236449 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00324382 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00636537 2.79507971 + vae.encoder_f1 0.00636991 2.79422116 + vae.decoder 0.00025538 0.05639221 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 2.58327418 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 122792 +BPFP 0.4345 bits/point +EBPFP 0.8689 equivalent bits/point +MSE 2.583274 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5833 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,352B, BPFP=0.7240 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,456B, BPFP=0.8487 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,020B, BPFP=0.6093 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,496B, BPFP=0.2822 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,496B, BPFP=0.2822 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,212B, BPFP=0.1591 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.94918998 + text_encoder-item0.clip_prompt_embeds 0.00023432 23.94796951 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.81648350 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.11644047 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00331256 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.23155926 5.12364292 + vae.encoder_f1 0.23156048 5.12171412 + vae.decoder 0.00018572 0.03711218 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 3.65951863 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 115412 +BPFP 0.4084 bits/point +EBPFP 0.8167 equivalent bits/point +MSE 3.659519 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6595 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,152B, BPFP=0.8323 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,860B, BPFP=0.8815 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,956B, BPFP=0.6837 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,840B, BPFP=0.3180 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,840B, BPFP=0.3180 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,556B, BPFP=0.2306 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.92589521 + text_encoder-item0.clip_prompt_embeds 0.00022528 23.92777242 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.93098631 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.12487290 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00319853 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00729824 3.08189273 + vae.encoder_f1 0.00730369 3.08265924 + vae.decoder 0.00019938 0.06681862 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 2.71656955 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 126592 +BPFP 0.4479 bits/point +EBPFP 0.8958 equivalent bits/point +MSE 2.716570 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7166 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,696B, BPFP=0.7706 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,012B, BPFP=0.8938 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 24,840B, BPFP=0.6301 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,412B, BPFP=0.2047 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,412B, BPFP=0.2047 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,696B, BPFP=0.2654 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.91777285 + text_encoder-item0.clip_prompt_embeds 0.00022149 23.93123055 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.90661049 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.13397918 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00239863 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00564371 1.80442083 + vae.encoder_f1 0.00565042 1.80473506 + vae.decoder 0.00019980 0.07600767 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 2.12543860 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 110480 +BPFP 0.3909 bits/point +EBPFP 0.7818 equivalent bits/point +MSE 2.125439 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1254 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,488B, BPFP=0.8777 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,152B, BPFP=0.9052 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,736B, BPFP=0.6528 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,244B, BPFP=0.2173 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,244B, BPFP=0.2173 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,580B, BPFP=0.2924 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.90015364 + text_encoder-item0.clip_prompt_embeds 0.00022173 23.95726799 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.93097534 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.21871599 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00264774 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00576096 1.64696240 + vae.encoder_f1 0.00576981 1.64633238 + vae.decoder 0.00019592 0.07309705 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 2.05627535 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114828 +BPFP 0.4063 bits/point +EBPFP 0.8126 equivalent bits/point +MSE 2.056275 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0563 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,564B, BPFP=0.8880 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,524B, BPFP=0.9354 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,840B, BPFP=0.7569 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,356B, BPFP=0.1885 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,356B, BPFP=0.1885 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,896B, BPFP=0.1799 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.93638118 + text_encoder-item0.clip_prompt_embeds 0.00025917 34.93605841 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.96450405 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.13600763 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00361832 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00594818 1.87497544 + vae.encoder_f1 0.00595328 1.87472022 + vae.decoder 0.00023462 0.05087871 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 2.44324176 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 111936 +BPFP 0.3961 bits/point +EBPFP 0.7921 equivalent bits/point +MSE 2.443242 +---------------------- -------------------------------------------------------- +Time: 0.739s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4432 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,176B, BPFP=0.9708 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,236B, BPFP=0.9120 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,960B, BPFP=0.7599 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,584B, BPFP=0.1920 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,584B, BPFP=0.1920 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,884B, BPFP=0.1490 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.90805984 + text_encoder-item0.clip_prompt_embeds 0.00022579 35.97949219 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.91401119 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.16031427 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00384470 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.85445058 7.23124504 + vae.encoder_f1 0.85445166 7.23127222 + vae.decoder 0.00025257 0.02433285 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 4.95264036 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 111832 +BPFP 0.3957 bits/point +EBPFP 0.7914 equivalent bits/point +MSE 4.952640 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.457s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.9526 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,232B, BPFP=0.8431 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,000B, BPFP=0.8929 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,508B, BPFP=0.6470 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,796B, BPFP=0.3478 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,796B, BPFP=0.3478 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,280B, BPFP=0.2832 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.438s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.93954388 + text_encoder-item0.clip_prompt_embeds 0.00025458 23.96035199 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.92514687 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.13684623 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00268806 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00628510 2.58958292 + vae.encoder_f1 0.00629234 2.58969927 + vae.decoder 0.00023521 0.07789175 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 2.49068921 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 131008 +BPFP 0.4635 bits/point +EBPFP 0.9271 equivalent bits/point +MSE 2.490689 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.438s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4907 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,680B, BPFP=0.8669 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,084B, BPFP=0.6870 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,800B, BPFP=0.2258 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,804B, BPFP=0.2259 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,520B, BPFP=0.2295 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.437s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.97767448 + text_encoder-item0.clip_prompt_embeds 0.00022807 23.93931573 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 1.00986891 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.12058989 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00393192 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00573429 1.80714881 + vae.encoder_f1 0.00574192 1.80755830 + vae.decoder 0.00017875 0.06089570 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 2.12489422 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114132 +BPFP 0.4038 bits/point +EBPFP 0.8077 equivalent bits/point +MSE 2.124894 +---------------------- -------------------------------------------------------- +Time: 0.736s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.437s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1249 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,460B, BPFP=1.0092 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,668B, BPFP=0.9471 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 37,244B, BPFP=0.9447 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,204B, BPFP=0.3083 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,204B, BPFP=0.3083 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,780B, BPFP=0.2374 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.438s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.91770617 + text_encoder-item0.clip_prompt_embeds 0.00027120 34.96352898 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.89880629 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.16719725 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00442290 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00781570 3.31092930 + vae.encoder_f1 0.00781878 3.31031370 + vae.decoder 0.00029724 0.07037102 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 3.11351458 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 137964 +BPFP 0.4882 bits/point +EBPFP 0.9763 equivalent bits/point +MSE 3.113515 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.438s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1135 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,464B, BPFP=0.8745 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,820B, BPFP=0.9594 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,484B, BPFP=0.7479 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,604B, BPFP=0.2686 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,604B, BPFP=0.2686 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,988B, BPFP=0.3353 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.95708966 + text_encoder-item0.clip_prompt_embeds 0.00022930 34.89980511 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.81547565 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.13374667 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00325211 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00577752 1.88577557 + vae.encoder_f1 0.00578475 1.88526547 + vae.decoder 0.00024190 0.07867203 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 2.45023865 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 127372 +BPFP 0.4507 bits/point +EBPFP 0.9014 equivalent bits/point +MSE 2.450239 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4502 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,644B, BPFP=0.8988 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,124B, BPFP=0.9029 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 30,236B, BPFP=0.7669 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,472B, BPFP=0.3276 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,472B, BPFP=0.3276 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,732B, BPFP=0.1444 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.99003704 + text_encoder-item0.clip_prompt_embeds 0.00028764 23.98071606 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 1.00480852 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.12935021 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00363175 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.03343784 4.14136887 + vae.encoder_f1 0.03344063 4.14278078 + vae.decoder 0.00016139 0.02989344 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 3.20549320 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 129068 +BPFP 0.4567 bits/point +EBPFP 0.9134 equivalent bits/point +MSE 3.205493 +---------------------- -------------------------------------------------------- +Time: 0.767s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2055 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,800B, BPFP=0.7846 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,612B, BPFP=0.8614 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,100B, BPFP=0.6620 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,476B, BPFP=0.3277 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,476B, BPFP=0.3277 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,344B, BPFP=0.2546 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.94486284 + text_encoder-item0.clip_prompt_embeds 0.00023094 23.94424927 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.88454590 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.12744388 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00315106 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00637455 2.92587972 + vae.encoder_f1 0.00637988 2.92553234 + vae.decoder 0.00020059 0.06826786 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 2.64464198 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 127208 +BPFP 0.4501 bits/point +EBPFP 0.9002 equivalent bits/point +MSE 2.644642 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6446 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,176B, BPFP=0.8355 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,020B, BPFP=0.8945 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,080B, BPFP=0.7123 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,876B, BPFP=0.2575 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,876B, BPFP=0.2575 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,384B, BPFP=0.2559 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.96959186 + text_encoder-item0.clip_prompt_embeds 0.00025217 23.94428732 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 1.03209095 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.13086605 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00352439 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00581597 1.85434699 + vae.encoder_f1 0.00582356 1.85452151 + vae.decoder 0.00019494 0.06740627 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 2.14801457 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 120816 +BPFP 0.4275 bits/point +EBPFP 0.8550 equivalent bits/point +MSE 2.148015 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1480 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 716B, BPFP=7.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,412B, BPFP=0.7321 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,532B, BPFP=0.7737 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,568B, BPFP=0.6485 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 10,984B, BPFP=0.1676 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 10,984B, BPFP=0.1676 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,652B, BPFP=0.1725 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.99352590 + text_encoder-item0.clip_prompt_embeds 0.00026975 23.94204038 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.94448957 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.11280751 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00307184 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 1.11695218 6.05053043 + vae.encoder_f1 1.11695278 6.04967594 + vae.decoder 0.00019720 0.05043061 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 4.09091329 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 101516 +BPFP 0.3592 bits/point +EBPFP 0.7184 equivalent bits/point +MSE 4.090913 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0909 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,320B, BPFP=0.8550 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,284B, BPFP=0.9159 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,392B, BPFP=0.7455 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,448B, BPFP=0.2815 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,444B, BPFP=0.2814 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,904B, BPFP=0.2412 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.94369245 + text_encoder-item0.clip_prompt_embeds 0.00025545 23.94648987 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.82373486 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.11683742 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00322552 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.01535016 3.52688074 + vae.encoder_f1 0.01535382 3.52612185 + vae.decoder 0.00021460 0.06178664 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 2.92209203 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 125180 +BPFP 0.4429 bits/point +EBPFP 0.8858 equivalent bits/point +MSE 2.922092 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9221 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,804B, BPFP=0.9205 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,060B, BPFP=0.8977 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,000B, BPFP=0.7102 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,836B, BPFP=0.2111 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,836B, BPFP=0.2111 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,608B, BPFP=0.2932 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.95495764 + text_encoder-item0.clip_prompt_embeds 0.00022628 23.95352028 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.94877110 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.13721140 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00368198 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00589589 1.63149714 + vae.encoder_f1 0.00590398 1.63158572 + vae.decoder 0.00017838 0.08846619 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 2.04757364 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 116548 +BPFP 0.4124 bits/point +EBPFP 0.8248 equivalent bits/point +MSE 2.047574 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0476 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,092B, BPFP=0.8241 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,080B, BPFP=0.8994 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,320B, BPFP=0.6422 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,392B, BPFP=0.3417 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,392B, BPFP=0.3417 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,360B, BPFP=0.1636 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.91433032 + text_encoder-item0.clip_prompt_embeds 0.00031548 23.94811536 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.93400612 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.13739471 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00282103 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00725484 3.50162029 + vae.encoder_f1 0.00725992 3.50131130 + vae.decoder 0.00019960 0.04445163 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 2.90940618 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 126020 +BPFP 0.4459 bits/point +EBPFP 0.8918 equivalent bits/point +MSE 2.909406 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9094 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,200B, BPFP=0.7035 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 9,504B, BPFP=0.7714 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 23,468B, BPFP=0.5953 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,772B, BPFP=0.3017 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,772B, BPFP=0.3017 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,416B, BPFP=0.1958 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.93807300 + text_encoder-item0.clip_prompt_embeds 0.00021831 144.57315341 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 1.10824051 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.11763276 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00279928 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00923516 3.12357950 + vae.encoder_f1 0.00923823 3.12349415 + vae.decoder 0.00019521 0.04057125 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 5.88786185 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 117536 +BPFP 0.4159 bits/point +EBPFP 0.8317 equivalent bits/point +MSE 5.887862 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.8879 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,720B, BPFP=0.7738 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,996B, BPFP=0.8925 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,268B, BPFP=0.7424 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,712B, BPFP=0.3160 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,712B, BPFP=0.3160 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,156B, BPFP=0.2184 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.93011427 + text_encoder-item0.clip_prompt_embeds 0.00062166 96.06972910 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.89883842 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.12416455 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00321192 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00831779 2.97977066 + vae.encoder_f1 0.00832197 2.97941566 + vae.decoder 0.00023271 0.05285780 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 4.55414914 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 127952 +BPFP 0.4527 bits/point +EBPFP 0.9055 equivalent bits/point +MSE 4.554149 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.5541 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,096B, BPFP=0.8247 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,712B, BPFP=0.8695 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,868B, BPFP=0.7069 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,568B, BPFP=0.2375 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,568B, BPFP=0.2375 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,216B, BPFP=0.1592 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.94746765 + text_encoder-item0.clip_prompt_embeds 0.00022938 23.95260713 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.89992113 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.12026917 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00338437 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00626977 2.34134245 + vae.encoder_f1 0.00627489 2.34046507 + vae.decoder 0.00017842 0.05376314 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 2.37169560 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114428 +BPFP 0.4049 bits/point +EBPFP 0.8098 equivalent bits/point +MSE 2.371696 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3717 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,672B, BPFP=0.9026 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,084B, BPFP=0.8997 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,716B, BPFP=0.7284 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,848B, BPFP=0.2418 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,852B, BPFP=0.2419 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,720B, BPFP=0.2661 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.87804802 + text_encoder-item0.clip_prompt_embeds 0.00022180 35.97835498 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.89294500 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.13705883 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00363033 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00585720 1.93750107 + vae.encoder_f1 0.00586586 1.93775511 + vae.decoder 0.00016520 0.07783050 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 2.50273042 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 120296 +BPFP 0.4256 bits/point +EBPFP 0.8513 equivalent bits/point +MSE 2.502730 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5027 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,940B, BPFP=0.8036 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,844B, BPFP=0.8802 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,252B, BPFP=0.6913 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,704B, BPFP=0.2091 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,704B, BPFP=0.2091 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,724B, BPFP=0.1747 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.95577192 + text_encoder-item0.clip_prompt_embeds 0.00025784 23.93944044 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.76037216 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.12135769 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00308562 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00734802 2.81556892 + vae.encoder_f1 0.00734987 2.81593871 + vae.decoder 0.00018093 0.03928421 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 2.58982242 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 110556 +BPFP 0.3912 bits/point +EBPFP 0.7824 equivalent bits/point +MSE 2.589822 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5898 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,696B, BPFP=0.9058 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,444B, BPFP=0.9289 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,276B, BPFP=0.7172 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,908B, BPFP=0.3343 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,908B, BPFP=0.3343 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,892B, BPFP=0.2103 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.94335349 + text_encoder-item0.clip_prompt_embeds 0.00023510 156.96306818 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.87551193 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.14941153 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00310181 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00637359 2.93842912 + vae.encoder_f1 0.00637830 2.93750906 + vae.decoder 0.00018566 0.06401458 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 6.12987391 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 130516 +BPFP 0.4618 bits/point +EBPFP 0.9236 equivalent bits/point +MSE 6.129874 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.1299 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,024B, BPFP=0.8149 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,296B, BPFP=0.9169 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,664B, BPFP=0.7271 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,424B, BPFP=0.2506 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,424B, BPFP=0.2506 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,880B, BPFP=0.1489 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.92882562 + text_encoder-item0.clip_prompt_embeds 0.00026418 59.46081067 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.89317408 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.13005736 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00308635 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.01530954 3.37827373 + vae.encoder_f1 0.01531230 3.38023615 + vae.decoder 0.00017892 0.03727905 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 3.78042673 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 117112 +BPFP 0.4144 bits/point +EBPFP 0.8287 equivalent bits/point +MSE 3.780427 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7804 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 720B, BPFP=7.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,548B, BPFP=0.8858 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,588B, BPFP=0.9406 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,180B, BPFP=0.7148 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,472B, BPFP=0.3124 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,472B, BPFP=0.3124 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,424B, BPFP=0.2266 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.89459713 + text_encoder-item0.clip_prompt_embeds 0.00021481 23.95045319 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.83880749 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.13542601 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00373823 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00591154 2.51572800 + vae.encoder_f1 0.00591973 2.51556826 + vae.decoder 0.00025286 0.06472340 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 2.45460840 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 128064 +BPFP 0.4531 bits/point +EBPFP 0.9062 equivalent bits/point +MSE 2.454608 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4546 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,476B, BPFP=0.8761 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,184B, BPFP=7.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,068B, BPFP=0.8984 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 30,564B, BPFP=0.7753 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,028B, BPFP=0.1988 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,028B, BPFP=0.1988 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,712B, BPFP=0.2659 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.94081839 + text_encoder-item0.clip_prompt_embeds 0.00023458 45.87281859 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.92754860 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.14551398 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00321259 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00588703 1.39957166 + vae.encoder_f1 0.00589573 1.39970410 + vae.decoder 0.00053402 0.07078428 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 2.51155059 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 116260 +BPFP 0.4114 bits/point +EBPFP 0.8227 equivalent bits/point +MSE 2.511551 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5116 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,048B, BPFP=0.9535 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,028B, BPFP=0.8951 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 30,096B, BPFP=0.7634 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,012B, BPFP=0.2901 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,016B, BPFP=0.2902 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,768B, BPFP=0.1760 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.92706347 + text_encoder-item0.clip_prompt_embeds 0.00022882 34.88478676 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.95120201 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.12463380 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00295117 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00659691 2.93343282 + vae.encoder_f1 0.00660300 2.93207932 + vae.decoder 0.00023739 0.04591537 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 2.93134984 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 125368 +BPFP 0.4436 bits/point +EBPFP 0.8872 equivalent bits/point +MSE 2.931350 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9313 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,288B, BPFP=0.8506 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,868B, BPFP=0.8821 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,300B, BPFP=0.6671 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,068B, BPFP=0.1841 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,068B, BPFP=0.1841 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,360B, BPFP=0.2856 +⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.94744118 + text_encoder-item0.clip_prompt_embeds 0.00023928 23.94288800 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.93944969 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.12624249 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00322803 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00583864 1.41783857 + vae.encoder_f1 0.00583800 1.41783047 + vae.decoder 0.00018889 0.07847898 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 1.94647786 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 110352 +BPFP 0.3905 bits/point +EBPFP 0.7809 equivalent bits/point +MSE 1.946478 +---------------------- -------------------------------------------------------- +Time: 0.760s Load: 0.008s, Pack+Encode: 0.299s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9465 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,788B, BPFP=0.9183 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,040B, BPFP=0.8961 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 31,080B, BPFP=0.7884 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 10,872B, BPFP=0.1659 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 10,872B, BPFP=0.1659 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,416B, BPFP=0.2263 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.92683331 + text_encoder-item0.clip_prompt_embeds 0.00024821 23.94847259 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.73711042 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.12326753 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00445593 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00570467 1.13493145 + vae.encoder_f1 0.00570488 1.13488328 + vae.decoder 0.00017302 0.05981145 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 1.81316704 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 111464 +BPFP 0.3944 bits/point +EBPFP 0.7888 equivalent bits/point +MSE 1.813167 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8132 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,436B, BPFP=0.8707 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,060B, BPFP=0.8977 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,892B, BPFP=0.7075 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,624B, BPFP=0.1926 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,624B, BPFP=0.1926 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,508B, BPFP=0.1376 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.92735100 + text_encoder-item0.clip_prompt_embeds 0.00021458 24.22180651 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.80404949 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.12917483 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00327194 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00914783 2.77752328 + vae.encoder_f1 0.00914958 2.77746820 + vae.decoder 0.00017527 0.03746323 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 2.57963550 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 108532 +BPFP 0.3840 bits/point +EBPFP 0.7680 equivalent bits/point +MSE 2.579635 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5796 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,992B, BPFP=0.9459 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,040B, BPFP=0.8961 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,836B, BPFP=0.7568 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,616B, BPFP=0.1925 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,616B, BPFP=0.1925 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,252B, BPFP=0.3129 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.95068192 + text_encoder-item0.clip_prompt_embeds 0.00022150 23.94016124 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.94726162 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.14475061 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00336107 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00578482 1.43715274 + vae.encoder_f1 0.00579739 1.43684483 + vae.decoder 0.00017668 0.08250174 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 1.95659161 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 116756 +BPFP 0.4131 bits/point +EBPFP 0.8262 equivalent bits/point +MSE 1.956592 +---------------------- -------------------------------------------------------- +Time: 0.761s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9566 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,144B, BPFP=0.8312 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,628B, BPFP=0.8627 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,056B, BPFP=0.6863 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,608B, BPFP=0.2229 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,604B, BPFP=0.2228 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,404B, BPFP=0.2260 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.439s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.95121233 + text_encoder-item0.clip_prompt_embeds 0.00023894 23.94528925 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.92296820 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.12001479 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00337827 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00958025 3.00654221 + vae.encoder_f1 0.00958229 3.00660229 + vae.decoder 0.00019995 0.06026506 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 2.68097625 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 113836 +BPFP 0.4028 bits/point +EBPFP 0.8056 equivalent bits/point +MSE 2.680976 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.439s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6810 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,040B, BPFP=0.9524 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,968B, BPFP=0.8903 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 30,596B, BPFP=0.7761 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,804B, BPFP=0.1496 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,804B, BPFP=0.1496 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,456B, BPFP=0.2886 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.92901889 + text_encoder-item0.clip_prompt_embeds 0.00023387 34.93811934 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.92658672 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.15473623 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00436072 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00567713 1.16603839 + vae.encoder_f1 0.00567905 1.16603017 + vae.decoder 0.00019376 0.09078687 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 2.12009357 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 111068 +BPFP 0.3930 bits/point +EBPFP 0.7860 equivalent bits/point +MSE 2.120094 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1201 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,676B, BPFP=0.9031 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,796B, BPFP=0.9575 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,352B, BPFP=0.7445 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,016B, BPFP=0.2291 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,012B, BPFP=0.2291 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,664B, BPFP=0.2034 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.91278513 + text_encoder-item0.clip_prompt_embeds 0.00024281 23.95470821 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.88318129 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.12257521 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00348998 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.02387581 2.59121561 + vae.encoder_f1 0.02387858 2.59078813 + vae.decoder 0.00018648 0.05001713 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 2.48739776 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 117924 +BPFP 0.4172 bits/point +EBPFP 0.8345 equivalent bits/point +MSE 2.487398 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4874 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,248B, BPFP=0.8452 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,668B, BPFP=0.8659 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,596B, BPFP=0.7253 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,012B, BPFP=0.3511 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,016B, BPFP=0.3512 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,520B, BPFP=0.1685 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.94917250 + text_encoder-item0.clip_prompt_embeds 0.00022399 23.94595509 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.87171583 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.12988346 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00341682 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.01169517 3.83196354 + vae.encoder_f1 0.01169969 3.83244538 + vae.decoder 0.00021186 0.03621769 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 3.06151332 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 130456 +BPFP 0.4616 bits/point +EBPFP 0.9232 equivalent bits/point +MSE 3.061513 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0615 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,280B, BPFP=0.8496 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,356B, BPFP=0.8406 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 31,140B, BPFP=0.7899 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,856B, BPFP=0.2572 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,856B, BPFP=0.2572 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,208B, BPFP=0.2810 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.91457836 + text_encoder-item0.clip_prompt_embeds 0.00022123 48.35028155 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.86974726 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.12093570 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00382086 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.32749966 7.18941593 + vae.encoder_f1 0.32750070 7.18985605 + vae.decoder 0.00039956 0.06474409 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 5.25983638 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 124084 +BPFP 0.4390 bits/point +EBPFP 0.8781 equivalent bits/point +MSE 5.259836 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.2598 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,356B, BPFP=0.8598 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,392B, BPFP=0.9247 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,668B, BPFP=0.6764 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,384B, BPFP=0.2042 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,384B, BPFP=0.2042 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,072B, BPFP=0.2463 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.89397510 + text_encoder-item0.clip_prompt_embeds 0.00024675 23.94587054 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.85555744 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.12648292 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00287454 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00566967 1.69204879 + vae.encoder_f1 0.00567867 1.69186091 + vae.decoder 0.00017839 0.06483991 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 2.07199832 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 112644 +BPFP 0.3986 bits/point +EBPFP 0.7971 equivalent bits/point +MSE 2.071998 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0720 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 748B, BPFP=7.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,308B, BPFP=0.7181 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,064B, BPFP=0.8169 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,476B, BPFP=0.6969 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,000B, BPFP=0.1678 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,000B, BPFP=0.1678 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,192B, BPFP=0.3110 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.88045343 + text_encoder-item0.clip_prompt_embeds 0.00022364 192.69277597 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 1.00277996 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.11633131 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00303855 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00580750 1.36961448 + vae.encoder_f1 0.00580664 1.36960614 + vae.decoder 0.00018044 0.09161040 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 6.33882510 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 108464 +BPFP 0.3838 bits/point +EBPFP 0.7675 equivalent bits/point +MSE 6.338825 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3388 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,212B, BPFP=0.8404 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,524B, BPFP=0.9354 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 26,148B, BPFP=0.6633 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,860B, BPFP=0.2420 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,860B, BPFP=0.2420 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,436B, BPFP=0.1659 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.92038218 + text_encoder-item0.clip_prompt_embeds 0.00030118 108.40493101 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.82953463 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.15629459 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00290913 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.03869025 3.87183452 + vae.encoder_f1 0.03869358 3.87184238 + vae.decoder 0.00021614 0.04693230 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 5.29119873 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 114440 +BPFP 0.4049 bits/point +EBPFP 0.8098 equivalent bits/point +MSE 5.291199 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.2912 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,460B, BPFP=0.7386 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 10,624B, BPFP=0.8623 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 25,352B, BPFP=0.6431 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,816B, BPFP=0.3481 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,812B, BPFP=0.3481 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,024B, BPFP=0.1533 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.95306961 + text_encoder-item0.clip_prompt_embeds 0.00023260 23.94839227 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.97482805 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.12089771 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00319252 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00839879 3.81455112 + vae.encoder_f1 0.00840224 3.81420183 + vae.decoder 0.00019463 0.04420110 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 3.05387133 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 125488 +BPFP 0.4440 bits/point +EBPFP 0.8880 equivalent bits/point +MSE 3.053871 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0539 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,412B, BPFP=1.0027 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,240B, BPFP=0.9123 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 29,376B, BPFP=0.7451 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,888B, BPFP=0.3035 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,884B, BPFP=0.3034 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,012B, BPFP=0.1835 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.93482288 + text_encoder-item0.clip_prompt_embeds 0.00023544 23.93684262 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.94495420 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.15283854 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00350402 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.01160815 3.49643040 + vae.encoder_f1 0.01161249 3.49865580 + vae.decoder 0.00021720 0.04436514 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 2.90806374 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 127212 +BPFP 0.4501 bits/point +EBPFP 0.9002 equivalent bits/point +MSE 2.908064 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9081 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,012B, BPFP=0.9486 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,224B, BPFP=0.9110 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 27,464B, BPFP=0.6966 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,688B, BPFP=0.1936 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,688B, BPFP=0.1936 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,576B, BPFP=0.1702 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.90647372 + text_encoder-item0.clip_prompt_embeds 0.00022923 23.92745536 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.91088858 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.12287991 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00343066 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.02989292 3.70821238 + vae.encoder_f1 0.02989391 3.70740366 + vae.decoder 0.00034944 0.04384273 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 3.00392672 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 110072 +BPFP 0.3895 bits/point +EBPFP 0.7789 equivalent bits/point +MSE 3.003927 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0039 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,468B, BPFP=0.8750 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,236B, BPFP=0.9120 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 28,124B, BPFP=0.7134 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,608B, BPFP=0.2992 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,608B, BPFP=0.2992 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,884B, BPFP=0.2101 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.440s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.95712709 + text_encoder-item0.clip_prompt_embeds 0.00024627 23.96090791 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.98602676 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.14285280 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00342156 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 + vae.encoder_f0 0.00613025 2.19635606 + vae.encoder_f1 0.00613536 2.19571853 + vae.decoder 0.00018697 0.05836669 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 2.30630367 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 125316 +BPFP 0.4434 bits/point +EBPFP 0.8868 equivalent bits/point +MSE 2.306304 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.440s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3063 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.4208 bits/point +Avg EBPFP 0.8416 equivalent bits/point +Avg MSE 3.199663 +Avg Time 0.755s +------------------------ ---------------------------- diff --git a/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log b/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..331f6f73ddfca9c4e77388bd43cf4c9a06e825f4 --- /dev/null +++ b/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log @@ -0,0 +1,21862 @@ +Experiment: dtufc_elic-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: sd35 + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 333 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 900B, BPFP=9.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,476B, BPFP=1.1466 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,352B, BPFP=8.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,492B, BPFP=1.0951 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,392B, BPFP=1.2528 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,848B, BPFP=0.4707 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,848B, BPFP=0.4707 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,576B, BPFP=0.4448 +⌛️ [2/4] FRONTEND: Frontend time: 3.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.653s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.70984848 + text_encoder-item0.clip_prompt_embeds 0.00025464 23.81713339 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.62970324 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.09885127 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00400369 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00635250 1.50902414 + vae.encoder_f1 0.00635834 1.50872231 + vae.decoder 0.00019940 0.03976070 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 1.95946393 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 196536 +BPFP 0.6954 bits/point +EBPFP 1.3908 equivalent bits/point +MSE 1.959464 +---------------------- -------------------------------------------------------- +Time: 4.806s Load: 0.007s, Pack+Encode: 3.147s, Decode+Unpack: 1.653s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9595 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 908B, BPFP=9.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,072B, BPFP=1.2273 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,380B, BPFP=8.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,884B, BPFP=1.1269 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,920B, BPFP=1.1648 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,364B, BPFP=0.3412 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,364B, BPFP=0.3412 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,736B, BPFP=0.3582 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.65110834 + text_encoder-item0.clip_prompt_embeds 0.00022609 36.12279745 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.68932552 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.10339386 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00377512 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.01130640 1.54237771 + vae.encoder_f1 0.01130902 1.54257166 + vae.decoder 0.00020860 0.03585506 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 2.29662763 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 174280 +BPFP 0.6166 bits/point +EBPFP 1.2333 equivalent bits/point +MSE 2.296628 +---------------------- -------------------------------------------------------- +Time: 3.760s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2966 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 956B, BPFP=9.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,052B, BPFP=0.9540 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,480B, BPFP=9.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,548B, BPFP=1.0185 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,448B, BPFP=1.2035 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,004B, BPFP=0.1832 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,004B, BPFP=0.1832 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,440B, BPFP=0.2576 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.64717897 + text_encoder-item0.clip_prompt_embeds 0.00022402 24.33366308 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 0.78386412 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.09064315 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00283012 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 1.19630027 4.03024387 + vae.encoder_f1 1.19630098 4.02875948 + vae.decoder 0.00023596 0.03028558 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 3.14040673 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 148584 +BPFP 0.5257 bits/point +EBPFP 1.0515 equivalent bits/point +MSE 3.140407 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1404 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 964B, BPFP=10.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,844B, BPFP=1.0611 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,412B, BPFP=8.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,724B, BPFP=1.1140 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,368B, BPFP=1.2015 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,192B, BPFP=0.4149 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,188B, BPFP=0.4149 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,660B, BPFP=0.5695 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.63897828 + text_encoder-item0.clip_prompt_embeds 0.00030342 23.88409556 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.63470650 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.13742787 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00326116 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00586287 1.07838476 + vae.encoder_f1 0.00587438 1.07704449 + vae.decoder 0.00017677 0.05893368 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 1.76503745 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 191004 +BPFP 0.6758 bits/point +EBPFP 1.3516 equivalent bits/point +MSE 1.765037 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7650 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 920B, BPFP=9.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,052B, BPFP=0.9540 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,396B, BPFP=8.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,176B, BPFP=0.9883 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,588B, BPFP=1.0803 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,780B, BPFP=0.3018 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,784B, BPFP=0.3019 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,204B, BPFP=0.3114 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.62277563 + text_encoder-item0.clip_prompt_embeds 0.00024120 34.79864634 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.69566326 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.09315898 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00357640 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00779453 1.31320775 + vae.encoder_f1 0.00779802 1.31361794 + vae.decoder 0.00023829 0.03493879 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 2.15517688 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 160552 +BPFP 0.5681 bits/point +EBPFP 1.1362 equivalent bits/point +MSE 2.155177 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.007s, Pack+Encode: 2.137s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1552 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 908B, BPFP=9.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,556B, BPFP=1.0222 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,420B, BPFP=8.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,912B, BPFP=1.0481 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,032B, BPFP=1.1422 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,416B, BPFP=0.4641 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,412B, BPFP=0.4641 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,800B, BPFP=0.3906 +⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.66842564 + text_encoder-item0.clip_prompt_embeds 0.00025651 23.90909936 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.68356280 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.09711559 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00255799 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00655775 1.39402795 + vae.encoder_f1 0.00656268 1.39537299 + vae.decoder 0.00020283 0.03551923 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 1.90816694 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188108 +BPFP 0.6656 bits/point +EBPFP 1.3312 equivalent bits/point +MSE 1.908167 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.008s, Pack+Encode: 2.154s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9082 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 900B, BPFP=9.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,464B, BPFP=0.8745 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,380B, BPFP=8.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,572B, BPFP=0.9393 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,784B, BPFP=1.0345 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,656B, BPFP=0.4525 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,656B, BPFP=0.4525 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,888B, BPFP=0.4849 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.68342845 + text_encoder-item0.clip_prompt_embeds 0.00022242 73.46257779 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.72541790 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.09414659 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00497935 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00593415 1.20754004 + vae.encoder_f1 0.00594307 1.20986938 + vae.decoder 0.00018992 0.04921057 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 3.11979841 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 182952 +BPFP 0.6473 bits/point +EBPFP 1.2947 equivalent bits/point +MSE 3.119798 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.009s, Pack+Encode: 2.149s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1198 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 896B, BPFP=9.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,988B, BPFP=1.0806 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,408B, BPFP=8.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,472B, BPFP=1.0935 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,980B, BPFP=1.1917 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,360B, BPFP=0.4022 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,364B, BPFP=0.4023 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,568B, BPFP=0.3835 +⌛️ [2/4] FRONTEND: Frontend time: 2.166s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.67088207 + text_encoder-item0.clip_prompt_embeds 0.00022110 45.18213102 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.66204414 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.09971256 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00360602 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00641770 1.17656589 + vae.encoder_f1 0.00642053 1.17823815 + vae.decoder 0.00017498 0.02968689 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 2.36335651 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 182688 +BPFP 0.6464 bits/point +EBPFP 1.2928 equivalent bits/point +MSE 2.363357 +---------------------- -------------------------------------------------------- +Time: 3.774s Load: 0.008s, Pack+Encode: 2.166s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3634 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 932B, BPFP=9.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,288B, BPFP=0.8506 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,424B, BPFP=8.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,124B, BPFP=0.9841 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,968B, BPFP=1.1406 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,620B, BPFP=0.3299 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,604B, BPFP=0.3297 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,956B, BPFP=0.4564 +⌛️ [2/4] FRONTEND: Frontend time: 2.174s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.65755232 + text_encoder-item0.clip_prompt_embeds 0.00021654 23.87235144 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.69560103 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.09024512 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00462838 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00577698 1.07932997 + vae.encoder_f1 0.00578348 1.08102381 + vae.decoder 0.00017559 0.04683327 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 1.76264399 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170568 +BPFP 0.6035 bits/point +EBPFP 1.2070 equivalent bits/point +MSE 1.762644 +---------------------- -------------------------------------------------------- +Time: 3.783s Load: 0.007s, Pack+Encode: 2.174s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7626 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 868B, BPFP=9.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,136B, BPFP=0.9654 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,340B, BPFP=8.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,084B, BPFP=1.0620 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,784B, BPFP=1.1867 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,272B, BPFP=0.4009 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,272B, BPFP=0.4009 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,876B, BPFP=0.3624 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.72024266 + text_encoder-item0.clip_prompt_embeds 0.00022160 34.81755826 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.61265483 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.09532151 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00415438 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00668450 1.13030481 + vae.encoder_f1 0.00668875 1.13020539 + vae.decoder 0.00023059 0.03737789 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 2.07117260 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 180284 +BPFP 0.6379 bits/point +EBPFP 1.2758 equivalent bits/point +MSE 2.071173 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0712 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 896B, BPFP=9.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,168B, BPFP=1.1050 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,336B, BPFP=8.3500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,348B, BPFP=1.0834 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,420B, BPFP=1.2028 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,516B, BPFP=0.3588 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,516B, BPFP=0.3588 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,548B, BPFP=0.2303 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.69143907 + text_encoder-item0.clip_prompt_embeds 0.00023190 232.91210092 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.58036528 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.09535979 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00379865 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.04018118 1.84212816 + vae.encoder_f1 0.04018488 1.84073138 + vae.decoder 0.00016201 0.02387042 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 7.58049186 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 172400 +BPFP 0.6100 bits/point +EBPFP 1.2200 equivalent bits/point +MSE 7.580492 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.5805 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 896B, BPFP=9.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,996B, BPFP=0.9464 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,352B, BPFP=8.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,308B, BPFP=0.9990 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,292B, BPFP=1.1996 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,512B, BPFP=0.4045 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,508B, BPFP=0.4045 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,252B, BPFP=0.4044 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.615s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.72161237 + text_encoder-item0.clip_prompt_embeds 0.00023140 23.81963609 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.65653458 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.09870881 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00482297 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.04874706 1.86911798 + vae.encoder_f1 0.04875064 1.87068546 + vae.decoder 0.00019641 0.02894654 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 2.12583634 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 181768 +BPFP 0.6431 bits/point +EBPFP 1.2863 equivalent bits/point +MSE 2.125836 +---------------------- -------------------------------------------------------- +Time: 3.770s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.615s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1258 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 908B, BPFP=9.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,932B, BPFP=1.0731 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,372B, BPFP=8.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,516B, BPFP=1.0159 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,484B, BPFP=1.1030 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,204B, BPFP=0.4761 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,220B, BPFP=0.4764 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,212B, BPFP=0.2201 +⌛️ [2/4] FRONTEND: Frontend time: 2.176s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.66245735 + text_encoder-item0.clip_prompt_embeds 0.00030893 23.88756849 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.65325155 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.09803197 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00372266 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.01360236 1.82046652 + vae.encoder_f1 0.01360807 1.83017588 + vae.decoder 0.00023006 0.03028120 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 2.10688788 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 182500 +BPFP 0.6457 bits/point +EBPFP 1.2915 equivalent bits/point +MSE 2.106888 +---------------------- -------------------------------------------------------- +Time: 3.789s Load: 0.009s, Pack+Encode: 2.176s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1069 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 916B, BPFP=9.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,256B, BPFP=1.1169 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,380B, BPFP=8.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,756B, BPFP=1.1166 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,280B, BPFP=1.2246 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,560B, BPFP=0.1764 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,556B, BPFP=0.1763 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,072B, BPFP=0.1243 +⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.67283670 + text_encoder-item0.clip_prompt_embeds 0.00024198 106.71115733 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.67054505 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.09342110 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00498245 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 1.67190456 4.63293600 + vae.encoder_f1 1.67190480 4.62825918 + vae.decoder 0.00017417 0.01420617 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 5.57225187 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 146428 +BPFP 0.5181 bits/point +EBPFP 1.0362 equivalent bits/point +MSE 5.572252 +---------------------- -------------------------------------------------------- +Time: 3.777s Load: 0.007s, Pack+Encode: 2.158s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.5723 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 920B, BPFP=9.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,320B, BPFP=0.9903 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,360B, BPFP=8.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,472B, BPFP=1.0123 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,444B, BPFP=1.2034 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,336B, BPFP=0.4781 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,344B, BPFP=0.4783 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,516B, BPFP=0.4430 +⌛️ [2/4] FRONTEND: Frontend time: 2.170s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.62759956 + text_encoder-item0.clip_prompt_embeds 0.00025129 23.70039189 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.67946949 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.09173312 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00379741 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00621760 1.39352846 + vae.encoder_f1 0.00622505 1.39139259 + vae.decoder 0.00025114 0.04244949 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 1.90239498 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 193364 +BPFP 0.6842 bits/point +EBPFP 1.3683 equivalent bits/point +MSE 1.902395 +---------------------- -------------------------------------------------------- +Time: 3.789s Load: 0.008s, Pack+Encode: 2.170s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9024 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 912B, BPFP=9.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,820B, BPFP=0.9226 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,400B, BPFP=8.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,200B, BPFP=0.9903 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,984B, BPFP=1.0903 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,620B, BPFP=0.5130 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,628B, BPFP=0.5131 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,804B, BPFP=0.4518 +⌛️ [2/4] FRONTEND: Frontend time: 2.171s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.67246175 + text_encoder-item0.clip_prompt_embeds 0.00020838 23.85563997 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.66910095 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.11913353 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00533168 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00675961 1.63489008 + vae.encoder_f1 0.00676652 1.62648559 + vae.decoder 0.00021373 0.04433124 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 2.01857370 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 193020 +BPFP 0.6830 bits/point +EBPFP 1.3659 equivalent bits/point +MSE 2.018574 +---------------------- -------------------------------------------------------- +Time: 3.786s Load: 0.008s, Pack+Encode: 2.171s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0186 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 916B, BPFP=9.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,232B, BPFP=0.9784 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,364B, BPFP=8.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,216B, BPFP=1.0727 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,932B, BPFP=1.2919 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,284B, BPFP=0.2790 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,276B, BPFP=0.2789 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,364B, BPFP=0.6825 +⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.62966013 + text_encoder-item0.clip_prompt_embeds 0.00021387 23.92710447 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.66208501 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.09445372 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00379604 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00596338 0.81004941 + vae.encoder_f1 0.00596322 0.81075621 + vae.decoder 0.00018207 0.06976702 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 1.64166133 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 179236 +BPFP 0.6342 bits/point +EBPFP 1.2684 equivalent bits/point +MSE 1.641661 +---------------------- -------------------------------------------------------- +Time: 3.772s Load: 0.008s, Pack+Encode: 2.163s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6417 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 908B, BPFP=9.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,440B, BPFP=1.0065 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,376B, BPFP=8.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,824B, BPFP=1.0409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,516B, BPFP=1.0784 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,192B, BPFP=0.2013 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,192B, BPFP=0.2013 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,676B, BPFP=0.5089 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.608s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.61025016 + text_encoder-item0.clip_prompt_embeds 0.00022138 23.86419229 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.64866495 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.10268521 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00610730 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00552804 0.65394646 + vae.encoder_f1 0.00552758 0.65411258 + vae.decoder 0.00018040 0.05477891 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 1.56642421 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 154776 +BPFP 0.5476 bits/point +EBPFP 1.0953 equivalent bits/point +MSE 1.566424 +---------------------- -------------------------------------------------------- +Time: 3.777s Load: 0.008s, Pack+Encode: 2.161s, Decode+Unpack: 1.608s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.5664 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 928B, BPFP=9.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,232B, BPFP=0.9784 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,388B, BPFP=8.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,380B, BPFP=1.0049 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,164B, BPFP=1.1202 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,404B, BPFP=0.2503 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,400B, BPFP=0.2502 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,680B, BPFP=0.2954 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.70683980 + text_encoder-item0.clip_prompt_embeds 0.00024507 23.93065983 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.68562322 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.10577878 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00567503 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00721525 1.07113969 + vae.encoder_f1 0.00721777 1.07260072 + vae.decoder 0.00018707 0.03077432 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 1.75928899 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 155228 +BPFP 0.5492 bits/point +EBPFP 1.0985 equivalent bits/point +MSE 1.759289 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.007s, Pack+Encode: 2.148s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7593 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 928B, BPFP=9.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,404B, BPFP=0.8663 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,440B, BPFP=9.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,824B, BPFP=0.9597 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,668B, BPFP=0.9808 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,612B, BPFP=0.3908 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,628B, BPFP=0.3911 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,888B, BPFP=0.3933 +⌛️ [2/4] FRONTEND: Frontend time: 2.157s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.64883320 + text_encoder-item0.clip_prompt_embeds 0.00046272 179.67008252 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.69755478 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.08996932 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00508005 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.01999603 1.67190921 + vae.encoder_f1 0.01999529 1.66708934 + vae.decoder 0.00024882 0.03906850 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 6.10997451 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170044 +BPFP 0.6017 bits/point +EBPFP 1.2033 equivalent bits/point +MSE 6.109975 +---------------------- -------------------------------------------------------- +Time: 3.769s Load: 0.007s, Pack+Encode: 2.157s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.1100 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 912B, BPFP=9.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,828B, BPFP=0.9237 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,424B, BPFP=8.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,200B, BPFP=0.9903 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,568B, BPFP=1.0290 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,272B, BPFP=0.4161 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,272B, BPFP=0.4161 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,168B, BPFP=0.2493 +⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.66528757 + text_encoder-item0.clip_prompt_embeds 0.00020334 23.81748639 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.60092964 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.08951306 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00495139 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.01341345 1.51214302 + vae.encoder_f1 0.01341645 1.50631583 + vae.decoder 0.00018350 0.02366232 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 1.95746558 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 171296 +BPFP 0.6061 bits/point +EBPFP 1.2122 equivalent bits/point +MSE 1.957466 +---------------------- -------------------------------------------------------- +Time: 3.773s Load: 0.008s, Pack+Encode: 2.163s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9575 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 924B, BPFP=9.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,660B, BPFP=0.9010 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,400B, BPFP=8.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,660B, BPFP=1.0276 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,476B, BPFP=1.2042 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,376B, BPFP=0.4482 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,368B, BPFP=0.4481 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,384B, BPFP=0.5610 +⌛️ [2/4] FRONTEND: Frontend time: 2.172s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.618s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.63529400 + text_encoder-item0.clip_prompt_embeds 0.00022316 23.89522668 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.68305101 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.09508518 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00527672 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00606298 1.14264464 + vae.encoder_f1 0.00607096 1.14264929 + vae.decoder 0.00023408 0.05035473 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 1.79290897 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 192900 +BPFP 0.6825 bits/point +EBPFP 1.3651 equivalent bits/point +MSE 1.792909 +---------------------- -------------------------------------------------------- +Time: 3.798s Load: 0.008s, Pack+Encode: 2.172s, Decode+Unpack: 1.618s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7929 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 904B, BPFP=9.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,132B, BPFP=0.9648 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,384B, BPFP=8.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,008B, BPFP=0.9747 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,612B, BPFP=1.1570 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,716B, BPFP=0.4077 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,716B, BPFP=0.4077 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,088B, BPFP=0.4910 +⌛️ [2/4] FRONTEND: Frontend time: 2.157s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.66789714 + text_encoder-item0.clip_prompt_embeds 0.00023597 23.66738112 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.68315578 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.11460076 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00365055 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00653100 1.26650560 + vae.encoder_f1 0.00653745 1.26678991 + vae.decoder 0.00020026 0.04245791 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 1.84417674 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183212 +BPFP 0.6483 bits/point +EBPFP 1.2965 equivalent bits/point +MSE 1.844177 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.157s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8442 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 916B, BPFP=9.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,500B, BPFP=1.0146 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,404B, BPFP=8.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,784B, BPFP=1.0377 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,020B, BPFP=1.1927 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,300B, BPFP=0.4166 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,296B, BPFP=0.4165 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,832B, BPFP=0.3000 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.585s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.65329695 + text_encoder-item0.clip_prompt_embeds 0.00022433 33.96283355 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.68832288 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.09607481 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00469135 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00869686 1.67687285 + vae.encoder_f1 0.00870063 1.68181264 + vae.decoder 0.00021246 0.03288947 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 2.30307415 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 180704 +BPFP 0.6394 bits/point +EBPFP 1.2788 equivalent bits/point +MSE 2.303074 +---------------------- -------------------------------------------------------- +Time: 3.736s Load: 0.009s, Pack+Encode: 2.142s, Decode+Unpack: 1.585s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3031 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 944B, BPFP=9.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,128B, BPFP=1.0996 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,396B, BPFP=8.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,580B, BPFP=1.1023 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,668B, BPFP=1.1330 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,044B, BPFP=0.4737 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,044B, BPFP=0.4737 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,120B, BPFP=0.3699 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.64568035 + text_encoder-item0.clip_prompt_embeds 0.00022433 23.77917428 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.65451164 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.09172255 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00419101 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00626512 1.50063264 + vae.encoder_f1 0.00626949 1.50152731 + vae.decoder 0.00018936 0.03499766 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 1.95401224 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 189576 +BPFP 0.6708 bits/point +EBPFP 1.3415 equivalent bits/point +MSE 1.954012 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9540 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 908B, BPFP=9.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,788B, BPFP=1.0536 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,376B, BPFP=8.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,640B, BPFP=1.1071 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,972B, BPFP=1.2168 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,048B, BPFP=0.3364 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,060B, BPFP=0.3366 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,496B, BPFP=0.2593 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.72649074 + text_encoder-item0.clip_prompt_embeds 0.00026137 60.37619639 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.62911091 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.10847297 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00378680 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.35915655 2.67083740 + vae.encoder_f1 0.35915723 2.66741776 + vae.decoder 0.00024181 0.02919270 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 3.45292044 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170940 +BPFP 0.6048 bits/point +EBPFP 1.2097 equivalent bits/point +MSE 3.452920 +---------------------- -------------------------------------------------------- +Time: 3.762s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4529 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 912B, BPFP=9.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,940B, BPFP=0.8036 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,400B, BPFP=8.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,180B, BPFP=0.9075 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,264B, BPFP=1.0720 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,524B, BPFP=0.1453 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,524B, BPFP=0.1453 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,024B, BPFP=0.2449 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.67816385 + text_encoder-item0.clip_prompt_embeds 0.00021656 72.84687838 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.70362701 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.09200594 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00556673 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.29031765 2.06558037 + vae.encoder_f1 0.29031771 2.05264807 + vae.decoder 0.00019965 0.03981583 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 3.49697292 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 135420 +BPFP 0.4792 bits/point +EBPFP 0.9583 equivalent bits/point +MSE 3.496973 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.007s, Pack+Encode: 2.139s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4970 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 904B, BPFP=9.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,596B, BPFP=0.7570 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,360B, BPFP=8.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,244B, BPFP=0.9127 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,308B, BPFP=1.0732 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,980B, BPFP=0.3506 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,980B, BPFP=0.3506 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,508B, BPFP=0.6259 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.69249551 + text_encoder-item0.clip_prompt_embeds 0.00025451 23.70206177 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.66826706 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.09367225 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00599418 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00595764 0.88208354 + vae.encoder_f1 0.00596395 0.88180172 + vae.decoder 0.00019845 0.05507310 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 1.66754701 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 174532 +BPFP 0.6175 bits/point +EBPFP 1.2351 equivalent bits/point +MSE 1.667547 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.009s, Pack+Encode: 2.149s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6675 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 924B, BPFP=9.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,568B, BPFP=1.0238 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,400B, BPFP=8.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,588B, BPFP=1.1029 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,664B, BPFP=1.1583 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,732B, BPFP=0.2706 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,736B, BPFP=0.2706 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,144B, BPFP=0.2485 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.64891338 + text_encoder-item0.clip_prompt_embeds 0.00026157 23.85423642 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.68021398 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.10497559 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00290491 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.40456498 2.78815055 + vae.encoder_f1 0.40456539 2.78351259 + vae.decoder 0.00020503 0.02815611 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 2.55142305 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 159408 +BPFP 0.5640 bits/point +EBPFP 1.1281 equivalent bits/point +MSE 2.551423 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5514 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 932B, BPFP=9.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,764B, BPFP=1.0503 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,380B, BPFP=8.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,600B, BPFP=0.9416 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,008B, BPFP=1.0148 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,192B, BPFP=0.4149 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,184B, BPFP=0.4148 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,804B, BPFP=0.5128 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.65464234 + text_encoder-item0.clip_prompt_embeds 0.00027179 23.90359510 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.69497209 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.11904557 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00292796 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00673531 1.65891266 + vae.encoder_f1 0.00673732 1.65866876 + vae.decoder 0.00020129 0.04780829 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 2.03293377 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 179516 +BPFP 0.6352 bits/point +EBPFP 1.2704 equivalent bits/point +MSE 2.032934 +---------------------- -------------------------------------------------------- +Time: 3.769s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.610s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0329 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 928B, BPFP=9.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,944B, BPFP=1.0747 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,348B, BPFP=8.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,784B, BPFP=1.0377 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,900B, BPFP=1.1896 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,840B, BPFP=0.3485 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,848B, BPFP=0.3486 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,604B, BPFP=0.2321 +⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.67229231 + text_encoder-item0.clip_prompt_embeds 0.00023057 23.60280328 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.70620728 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.10126773 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00420063 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00881784 1.50125694 + vae.encoder_f1 0.00882136 1.49888742 + vae.decoder 0.00017598 0.02419343 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 1.94813497 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 169848 +BPFP 0.6010 bits/point +EBPFP 1.2019 equivalent bits/point +MSE 1.948135 +---------------------- -------------------------------------------------------- +Time: 3.762s Load: 0.008s, Pack+Encode: 2.158s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9481 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 924B, BPFP=9.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,316B, BPFP=0.9897 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,352B, BPFP=8.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,388B, BPFP=1.0055 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,252B, BPFP=1.1478 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,104B, BPFP=0.3678 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,116B, BPFP=0.3680 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,580B, BPFP=0.5365 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.66343514 + text_encoder-item0.clip_prompt_embeds 0.00025208 23.88413783 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.69084301 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.10407437 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00501543 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00582247 0.92241579 + vae.encoder_f1 0.00582996 0.92238569 + vae.decoder 0.00016099 0.05157076 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 1.69098613 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 179684 +BPFP 0.6358 bits/point +EBPFP 1.2715 equivalent bits/point +MSE 1.690986 +---------------------- -------------------------------------------------------- +Time: 3.750s Load: 0.009s, Pack+Encode: 2.142s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6910 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 968B, BPFP=10.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,176B, BPFP=0.9708 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,368B, BPFP=8.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,584B, BPFP=1.0214 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,364B, BPFP=1.1253 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,620B, BPFP=0.4672 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,608B, BPFP=0.4670 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,248B, BPFP=0.4043 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.61292811 + text_encoder-item0.clip_prompt_embeds 0.00020809 96.93728017 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.68926582 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.10666706 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00317029 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00602745 1.51868021 + vae.encoder_f1 0.00603159 1.51984572 + vae.decoder 0.00017526 0.04094412 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 3.87709431 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 187588 +BPFP 0.6637 bits/point +EBPFP 1.3275 equivalent bits/point +MSE 3.877094 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8771 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 912B, BPFP=9.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,336B, BPFP=0.9924 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,368B, BPFP=8.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,308B, BPFP=0.9990 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,696B, BPFP=1.0576 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,140B, BPFP=0.4904 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,152B, BPFP=0.4906 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,120B, BPFP=0.4919 +⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.61893161 + text_encoder-item0.clip_prompt_embeds 0.00020908 23.80672940 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.70051723 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.11783483 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00409807 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00634616 1.45673990 + vae.encoder_f1 0.00635208 1.45543289 + vae.decoder 0.00022721 0.04137086 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 1.93574755 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 190684 +BPFP 0.6747 bits/point +EBPFP 1.3494 equivalent bits/point +MSE 1.935748 +---------------------- -------------------------------------------------------- +Time: 3.772s Load: 0.008s, Pack+Encode: 2.162s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9357 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 920B, BPFP=9.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,540B, BPFP=0.8847 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,416B, BPFP=8.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,424B, BPFP=0.9273 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,368B, BPFP=1.0747 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,952B, BPFP=0.2281 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,952B, BPFP=0.2281 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,092B, BPFP=0.1859 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.66404883 + text_encoder-item0.clip_prompt_embeds 0.00022947 23.91536881 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.72850451 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.10287669 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00321707 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.05448642 1.47558987 + vae.encoder_f1 0.05448771 1.47535264 + vae.decoder 0.00017748 0.02251796 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 1.94464946 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 145316 +BPFP 0.5142 bits/point +EBPFP 1.0283 equivalent bits/point +MSE 1.944649 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.007s, Pack+Encode: 2.148s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9446 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 916B, BPFP=9.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,952B, BPFP=0.9405 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,408B, BPFP=8.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,040B, BPFP=0.9773 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,500B, BPFP=1.1288 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,272B, BPFP=0.3398 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,276B, BPFP=0.3399 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,160B, BPFP=0.2185 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.66735578 + text_encoder-item0.clip_prompt_embeds 0.00020169 23.89724745 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.65703077 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.13715629 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00346675 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.06876971 1.74311626 + vae.encoder_f1 0.06877109 1.74076748 + vae.decoder 0.00023999 0.02030982 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 2.06898985 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 164176 +BPFP 0.5809 bits/point +EBPFP 1.1618 equivalent bits/point +MSE 2.068990 +---------------------- -------------------------------------------------------- +Time: 3.775s Load: 0.008s, Pack+Encode: 2.161s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0690 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 948B, BPFP=9.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,436B, BPFP=1.0060 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,396B, BPFP=8.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,632B, BPFP=1.0253 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,832B, BPFP=1.0357 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,248B, BPFP=0.3395 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,248B, BPFP=0.3395 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,692B, BPFP=0.5704 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.63327607 + text_encoder-item0.clip_prompt_embeds 0.00025253 23.67584889 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.70856562 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.09364499 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00285520 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00595097 0.88916731 + vae.encoder_f1 0.00595882 0.88956845 + vae.decoder 0.00020134 0.05877456 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 1.67029781 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 173084 +BPFP 0.6124 bits/point +EBPFP 1.2248 equivalent bits/point +MSE 1.670298 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6703 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 928B, BPFP=9.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,272B, BPFP=0.9838 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,428B, BPFP=8.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,420B, BPFP=1.0893 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,500B, BPFP=1.1795 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,132B, BPFP=0.2767 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,124B, BPFP=0.2766 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,044B, BPFP=0.4286 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.66094883 + text_encoder-item0.clip_prompt_embeds 0.00022201 24.12811993 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.70053487 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.10426516 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00433367 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00831743 1.16597867 + vae.encoder_f1 0.00831926 1.16817284 + vae.decoder 0.00028593 0.03724331 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 1.80909663 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 166500 +BPFP 0.5891 bits/point +EBPFP 1.1782 equivalent bits/point +MSE 1.809097 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.007s, Pack+Encode: 2.152s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8091 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 932B, BPFP=9.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,276B, BPFP=1.1196 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,452B, BPFP=9.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,844B, BPFP=1.0425 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,296B, BPFP=1.1743 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,744B, BPFP=0.4691 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,748B, BPFP=0.4692 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,748B, BPFP=0.3890 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.65065678 + text_encoder-item0.clip_prompt_embeds 0.00026808 23.94135763 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.68616123 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.13148318 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00409625 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00606586 1.46386492 + vae.encoder_f1 0.00607066 1.46331871 + vae.decoder 0.00019664 0.03833306 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 1.94299464 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 190692 +BPFP 0.6747 bits/point +EBPFP 1.3494 equivalent bits/point +MSE 1.942995 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9430 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 916B, BPFP=9.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,912B, BPFP=1.0703 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,376B, BPFP=8.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,144B, BPFP=1.1481 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,972B, BPFP=1.1915 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,160B, BPFP=0.3687 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,164B, BPFP=0.3687 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,644B, BPFP=0.3859 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.66474915 + text_encoder-item0.clip_prompt_embeds 0.00023198 60.30980705 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.75205584 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.09438438 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00434998 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.05216765 1.84857404 + vae.encoder_f1 0.05216896 1.84844017 + vae.decoder 0.00017960 0.03429428 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 3.07071095 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 178940 +BPFP 0.6331 bits/point +EBPFP 1.2663 equivalent bits/point +MSE 3.070711 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.009s, Pack+Encode: 2.145s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0707 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 888B, BPFP=9.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,504B, BPFP=1.0152 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,432B, BPFP=8.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,184B, BPFP=0.9890 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,148B, BPFP=1.0945 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,248B, BPFP=0.4921 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,236B, BPFP=0.4919 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,956B, BPFP=0.4259 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.68874971 + text_encoder-item0.clip_prompt_embeds 0.00023125 23.87931421 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.70219536 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.09935249 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00359211 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00620361 1.30829048 + vae.encoder_f1 0.00620966 1.31138873 + vae.decoder 0.00020748 0.04088050 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 1.86891296 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 190248 +BPFP 0.6731 bits/point +EBPFP 1.3463 equivalent bits/point +MSE 1.868913 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8689 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 924B, BPFP=9.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,148B, BPFP=0.9670 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,344B, BPFP=8.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,076B, BPFP=1.0614 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,088B, BPFP=1.1437 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,468B, BPFP=0.4191 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,488B, BPFP=0.4194 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,636B, BPFP=0.4467 +⌛️ [2/4] FRONTEND: Frontend time: 2.164s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.65112392 + text_encoder-item0.clip_prompt_embeds 0.00023066 23.80088482 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.66108613 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.09001473 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00539579 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.03159856 1.44868958 + vae.encoder_f1 0.03160188 1.45052087 + vae.decoder 0.00018417 0.03722901 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 1.93106562 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183824 +BPFP 0.6504 bits/point +EBPFP 1.3008 equivalent bits/point +MSE 1.931066 +---------------------- -------------------------------------------------------- +Time: 3.781s Load: 0.009s, Pack+Encode: 2.164s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9311 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 928B, BPFP=9.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,572B, BPFP=1.0244 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,360B, BPFP=8.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,592B, BPFP=1.0221 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,804B, BPFP=1.1618 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,836B, BPFP=0.5010 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,832B, BPFP=0.5010 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,696B, BPFP=0.3875 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.63660765 + text_encoder-item0.clip_prompt_embeds 0.00024948 23.85079943 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.65779867 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.10033614 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00416436 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.03490865 2.17162943 + vae.encoder_f1 0.03491008 2.17185545 + vae.decoder 0.00028462 0.04393181 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 2.26832384 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 193272 +BPFP 0.6838 bits/point +EBPFP 1.3677 equivalent bits/point +MSE 2.268324 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2683 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 948B, BPFP=9.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,440B, BPFP=0.8712 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,328B, BPFP=8.3000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,472B, BPFP=1.0123 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,732B, BPFP=1.2107 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,188B, BPFP=0.2318 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,188B, BPFP=0.2318 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,928B, BPFP=0.5471 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.61874922 + text_encoder-item0.clip_prompt_embeds 0.00021560 23.82276025 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.65676985 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.11000221 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00324687 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00544735 0.69754004 + vae.encoder_f1 0.00544843 0.69758594 + vae.decoder 0.00018632 0.05388018 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 1.58535322 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 163876 +BPFP 0.5798 bits/point +EBPFP 1.1597 equivalent bits/point +MSE 1.585353 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.009s, Pack+Encode: 2.140s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.5854 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 888B, BPFP=9.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,636B, BPFP=0.8977 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,376B, BPFP=8.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,208B, BPFP=0.9909 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,140B, BPFP=1.0943 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,704B, BPFP=0.4380 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,700B, BPFP=0.4379 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,948B, BPFP=0.4562 +⌛️ [2/4] FRONTEND: Frontend time: 2.133s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.65882933 + text_encoder-item0.clip_prompt_embeds 0.00022698 23.87809668 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.71122127 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.09139642 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00577974 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00630479 1.17971420 + vae.encoder_f1 0.00631430 1.17940915 + vae.decoder 0.00018596 0.03736136 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 1.80800763 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183252 +BPFP 0.6484 bits/point +EBPFP 1.2968 equivalent bits/point +MSE 1.808008 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.133s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8080 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 956B, BPFP=9.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,040B, BPFP=0.9524 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,408B, BPFP=8.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,924B, BPFP=0.9679 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,544B, BPFP=1.0284 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,516B, BPFP=0.4199 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,504B, BPFP=0.4197 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,092B, BPFP=0.5216 +⌛️ [2/4] FRONTEND: Frontend time: 2.160s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.67632929 + text_encoder-item0.clip_prompt_embeds 0.00024643 34.83845077 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.73379054 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.09615998 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00563932 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00612578 1.07242799 + vae.encoder_f1 0.00613243 1.07300532 + vae.decoder 0.00018179 0.04258982 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 2.04593617 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 180636 +BPFP 0.6391 bits/point +EBPFP 1.2783 equivalent bits/point +MSE 2.045936 +---------------------- -------------------------------------------------------- +Time: 3.769s Load: 0.008s, Pack+Encode: 2.160s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0459 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 892B, BPFP=9.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,432B, BPFP=1.1407 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,356B, BPFP=8.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,660B, BPFP=1.2711 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,396B, BPFP=1.3037 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 7,784B, BPFP=0.1188 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 7,784B, BPFP=0.1188 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,284B, BPFP=0.5580 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.67251563 + text_encoder-item0.clip_prompt_embeds 0.00024049 23.77334661 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.71138096 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.10321779 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00364363 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00526071 0.36027381 + vae.encoder_f1 0.00526072 0.36014608 + vae.decoder 0.00016981 0.05065428 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 1.42704198 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 158240 +BPFP 0.5599 bits/point +EBPFP 1.1198 equivalent bits/point +MSE 1.427042 +---------------------- -------------------------------------------------------- +Time: 3.766s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.4270 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 912B, BPFP=9.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,120B, BPFP=0.9632 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,380B, BPFP=8.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,664B, BPFP=0.9468 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,340B, BPFP=1.1247 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,996B, BPFP=0.4424 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,008B, BPFP=0.4426 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,760B, BPFP=0.5115 +⌛️ [2/4] FRONTEND: Frontend time: 2.167s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.60208738 + text_encoder-item0.clip_prompt_embeds 0.00022843 35.91218970 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.66875315 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.09336246 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00398839 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00622977 1.20732450 + vae.encoder_f1 0.00623684 1.20938301 + vae.decoder 0.00019755 0.04097058 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 2.13632185 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 186832 +BPFP 0.6611 bits/point +EBPFP 1.3221 equivalent bits/point +MSE 2.136322 +---------------------- -------------------------------------------------------- +Time: 3.778s Load: 0.008s, Pack+Encode: 2.167s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1363 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 900B, BPFP=9.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,884B, BPFP=1.0666 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,416B, BPFP=8.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,044B, BPFP=0.9776 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,412B, BPFP=1.0504 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,364B, BPFP=0.2955 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,368B, BPFP=0.2955 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,832B, BPFP=0.3306 +⌛️ [2/4] FRONTEND: Frontend time: 2.165s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.70842528 + text_encoder-item0.clip_prompt_embeds 0.00026004 23.86608200 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.72589030 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.09684534 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00357789 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00725303 0.97502923 + vae.encoder_f1 0.00725507 0.97437763 + vae.decoder 0.00017991 0.03051054 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 1.71184791 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 159872 +BPFP 0.5657 bits/point +EBPFP 1.1313 equivalent bits/point +MSE 1.711848 +---------------------- -------------------------------------------------------- +Time: 3.774s Load: 0.008s, Pack+Encode: 2.165s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7118 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 964B, BPFP=10.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,152B, BPFP=0.9675 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,436B, BPFP=8.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,880B, BPFP=1.0455 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,624B, BPFP=1.0812 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,180B, BPFP=0.3232 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,180B, BPFP=0.3232 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,116B, BPFP=0.2477 +⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.69122235 + text_encoder-item0.clip_prompt_embeds 0.00031748 23.79143627 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.70354090 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.09928303 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00551451 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.42111695 2.43819642 + vae.encoder_f1 0.42111716 2.43789840 + vae.decoder 0.00019827 0.02692112 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 2.38848958 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 162184 +BPFP 0.5739 bits/point +EBPFP 1.1477 equivalent bits/point +MSE 2.388490 +---------------------- -------------------------------------------------------- +Time: 3.767s Load: 0.008s, Pack+Encode: 2.162s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3885 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 928B, BPFP=9.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,528B, BPFP=1.0184 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,364B, BPFP=8.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,760B, BPFP=1.0357 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,196B, BPFP=1.2225 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,436B, BPFP=0.3729 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,420B, BPFP=0.3726 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,972B, BPFP=0.3043 +⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.62596194 + text_encoder-item0.clip_prompt_embeds 0.00024951 60.25946124 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.65415769 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.10605207 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00438542 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.10376993 2.55115318 + vae.encoder_f1 0.10377157 2.55277371 + vae.decoder 0.00019787 0.03031276 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 3.39561812 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 176256 +BPFP 0.6236 bits/point +EBPFP 1.2473 equivalent bits/point +MSE 3.395618 +---------------------- -------------------------------------------------------- +Time: 3.766s Load: 0.007s, Pack+Encode: 2.163s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3956 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 904B, BPFP=9.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,896B, BPFP=0.9329 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,364B, BPFP=8.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,884B, BPFP=1.0458 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,168B, BPFP=1.0950 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,036B, BPFP=0.3820 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,032B, BPFP=0.3820 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,652B, BPFP=0.2946 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.68030222 + text_encoder-item0.clip_prompt_embeds 0.00022350 23.80743329 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.64533463 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.09618148 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00303844 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.01346414 1.63084877 + vae.encoder_f1 0.01346933 1.63133073 + vae.decoder 0.00019243 0.02873999 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 2.01436037 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 171588 +BPFP 0.6071 bits/point +EBPFP 1.2142 equivalent bits/point +MSE 2.014360 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.008s, Pack+Encode: 2.153s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0144 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 880B, BPFP=9.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,476B, BPFP=1.0114 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,400B, BPFP=8.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,936B, BPFP=0.9688 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,056B, BPFP=1.1936 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,772B, BPFP=0.3322 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,772B, BPFP=0.3322 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,428B, BPFP=0.2572 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.67860381 + text_encoder-item0.clip_prompt_embeds 0.00024958 23.83779551 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.76596880 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.10474596 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00299617 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.11196710 2.18350697 + vae.encoder_f1 0.11196851 2.17909122 + vae.decoder 0.00023459 0.03238651 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 2.27118199 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 167372 +BPFP 0.5922 bits/point +EBPFP 1.1844 equivalent bits/point +MSE 2.271182 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2712 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 936B, BPFP=9.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,508B, BPFP=1.1510 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,380B, BPFP=8.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,036B, BPFP=1.0581 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,948B, BPFP=1.0387 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,280B, BPFP=0.4620 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,284B, BPFP=0.4621 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,516B, BPFP=0.3514 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.67636077 + text_encoder-item0.clip_prompt_embeds 0.00025929 23.87518601 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.69886389 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.13824687 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00539200 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00675017 1.48209131 + vae.encoder_f1 0.00675421 1.48131406 + vae.decoder 0.00023635 0.04101977 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 1.95046619 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183540 +BPFP 0.6494 bits/point +EBPFP 1.2988 equivalent bits/point +MSE 1.950466 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.009s, Pack+Encode: 2.140s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9505 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 952B, BPFP=9.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,752B, BPFP=0.9134 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,392B, BPFP=8.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,208B, BPFP=0.9909 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,004B, BPFP=1.1669 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,780B, BPFP=0.5307 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,764B, BPFP=0.5305 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,520B, BPFP=0.3516 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.60329572 + text_encoder-item0.clip_prompt_embeds 0.00064775 23.90163775 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.68601112 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.10307787 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00394838 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00728993 1.86453509 + vae.encoder_f1 0.00729572 1.86861253 + vae.decoder 0.00026488 0.04046166 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 2.12781774 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 195024 +BPFP 0.6900 bits/point +EBPFP 1.3801 equivalent bits/point +MSE 2.127818 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.009s, Pack+Encode: 2.144s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1278 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 940B, BPFP=9.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,016B, BPFP=0.9491 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,400B, BPFP=8.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,436B, BPFP=1.0094 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,192B, BPFP=1.1463 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,628B, BPFP=0.4521 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,624B, BPFP=0.4520 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,564B, BPFP=0.3834 +⌛️ [2/4] FRONTEND: Frontend time: 2.133s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.68523606 + text_encoder-item0.clip_prompt_embeds 0.00023188 23.89475320 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.63976669 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.10453180 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00475869 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00613207 1.38672996 + vae.encoder_f1 0.00613899 1.38597369 + vae.decoder 0.00023812 0.04157126 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 1.90523274 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 185452 +BPFP 0.6562 bits/point +EBPFP 1.3124 equivalent bits/point +MSE 1.905233 +---------------------- -------------------------------------------------------- +Time: 3.733s Load: 0.008s, Pack+Encode: 2.133s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9052 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 932B, BPFP=9.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,572B, BPFP=1.0244 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,384B, BPFP=8.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,696B, BPFP=1.0305 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,876B, BPFP=1.1383 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,548B, BPFP=0.3898 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,544B, BPFP=0.3898 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,464B, BPFP=0.3804 +⌛️ [2/4] FRONTEND: Frontend time: 2.159s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.62481876 + text_encoder-item0.clip_prompt_embeds 0.00023678 23.82859214 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.71249146 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.10472923 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00574633 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00636537 1.38174641 + vae.encoder_f1 0.00636991 1.38121510 + vae.decoder 0.00025538 0.03808530 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 1.90100612 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 177668 +BPFP 0.6286 bits/point +EBPFP 1.2573 equivalent bits/point +MSE 1.901006 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.159s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9010 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 912B, BPFP=9.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,036B, BPFP=0.9518 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,360B, BPFP=8.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,244B, BPFP=0.9938 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,672B, BPFP=1.0824 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,048B, BPFP=0.3822 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,060B, BPFP=0.3824 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,972B, BPFP=0.2433 +⌛️ [2/4] FRONTEND: Frontend time: 2.157s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.71785482 + text_encoder-item0.clip_prompt_embeds 0.00023432 24.73120011 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.61371851 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.08973418 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00605236 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.23155926 2.70130777 + vae.encoder_f1 0.23156048 2.69276357 + vae.decoder 0.00018572 0.02856049 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 2.53298655 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 168956 +BPFP 0.5978 bits/point +EBPFP 1.1956 equivalent bits/point +MSE 2.532987 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.008s, Pack+Encode: 2.157s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5330 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 908B, BPFP=9.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,980B, BPFP=0.9443 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,432B, BPFP=8.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,004B, BPFP=0.9744 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,600B, BPFP=1.1059 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,068B, BPFP=0.4741 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,068B, BPFP=0.4741 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,744B, BPFP=0.4500 +⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.66145647 + text_encoder-item0.clip_prompt_embeds 0.00022528 23.82866190 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.66802931 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.10871520 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00505825 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00729824 1.44903803 + vae.encoder_f1 0.00730369 1.45152640 + vae.decoder 0.00019938 0.04703691 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 1.93401878 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188456 +BPFP 0.6668 bits/point +EBPFP 1.3336 equivalent bits/point +MSE 1.934019 +---------------------- -------------------------------------------------------- +Time: 3.766s Load: 0.008s, Pack+Encode: 2.155s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9340 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 908B, BPFP=9.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,648B, BPFP=0.8994 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,376B, BPFP=8.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,336B, BPFP=1.0013 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,236B, BPFP=1.0206 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,392B, BPFP=0.3112 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,392B, BPFP=0.3112 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,688B, BPFP=0.5398 +⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.64146233 + text_encoder-item0.clip_prompt_embeds 0.00022149 23.88800604 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.69859319 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.10790329 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00595122 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00564371 0.98149574 + vae.encoder_f1 0.00565042 0.97979760 + vae.decoder 0.00019980 0.05278823 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 1.71853543 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 166628 +BPFP 0.5896 bits/point +EBPFP 1.1791 equivalent bits/point +MSE 1.718535 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.008s, Pack+Encode: 2.154s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7185 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 876B, BPFP=9.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,716B, BPFP=1.0438 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,400B, BPFP=8.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,180B, BPFP=1.0698 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,380B, BPFP=1.0242 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,640B, BPFP=0.3455 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,652B, BPFP=0.3456 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,100B, BPFP=0.5219 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.65323091 + text_encoder-item0.clip_prompt_embeds 0.00022173 23.71599576 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.69872274 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.10472867 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00388125 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00576096 0.89002532 + vae.encoder_f1 0.00576981 0.89042008 + vae.decoder 0.00019592 0.04849932 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 1.67118044 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 172596 +BPFP 0.6107 bits/point +EBPFP 1.2214 equivalent bits/point +MSE 1.671180 +---------------------- -------------------------------------------------------- +Time: 3.761s Load: 0.009s, Pack+Encode: 2.150s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6712 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 936B, BPFP=9.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,692B, BPFP=1.1759 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,384B, BPFP=8.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,484B, BPFP=1.0945 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,448B, BPFP=1.2289 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,188B, BPFP=0.2775 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,172B, BPFP=0.2773 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,336B, BPFP=0.3154 +⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.68616780 + text_encoder-item0.clip_prompt_embeds 0.00025917 23.84000651 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.69808049 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.11046505 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00412283 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00594818 0.97369456 + vae.encoder_f1 0.00595328 0.97342980 + vae.decoder 0.00023462 0.03481792 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 1.71178245 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 166292 +BPFP 0.5884 bits/point +EBPFP 1.1768 equivalent bits/point +MSE 1.711782 +---------------------- -------------------------------------------------------- +Time: 3.764s Load: 0.008s, Pack+Encode: 2.154s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7118 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 912B, BPFP=9.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,624B, BPFP=1.1667 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,384B, BPFP=8.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,296B, BPFP=1.2416 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,972B, BPFP=1.2676 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,044B, BPFP=0.2601 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,044B, BPFP=0.2601 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,056B, BPFP=0.1848 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.62862726 + text_encoder-item0.clip_prompt_embeds 0.00022579 73.22363704 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.67188439 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.15782798 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00383163 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.85445058 3.25520706 + vae.encoder_f1 0.85445166 3.24872732 + vae.decoder 0.00025257 0.01666692 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 4.05994270 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 162984 +BPFP 0.5767 bits/point +EBPFP 1.1534 equivalent bits/point +MSE 4.059943 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.007s, Pack+Encode: 2.146s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0599 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 928B, BPFP=9.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,224B, BPFP=0.9773 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,376B, BPFP=8.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,664B, BPFP=1.0279 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,140B, BPFP=1.0689 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,840B, BPFP=0.5164 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,824B, BPFP=0.5161 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,724B, BPFP=0.5409 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.63616323 + text_encoder-item0.clip_prompt_embeds 0.00025458 23.88770165 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.69458194 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.10917599 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00447979 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00628510 1.37157393 + vae.encoder_f1 0.00629234 1.37149012 + vae.decoder 0.00023521 0.05122186 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 1.89947218 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 196372 +BPFP 0.6948 bits/point +EBPFP 1.3896 equivalent bits/point +MSE 1.899472 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.009s, Pack+Encode: 2.142s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8995 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 944B, BPFP=9.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,220B, BPFP=0.9767 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,408B, BPFP=8.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,112B, BPFP=0.9831 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,136B, BPFP=1.1956 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,320B, BPFP=0.3558 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,312B, BPFP=0.3557 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,744B, BPFP=0.4500 +⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.70591124 + text_encoder-item0.clip_prompt_embeds 0.00022807 23.85661230 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.74175735 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.09303632 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00434267 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00573429 0.96633780 + vae.encoder_f1 0.00574192 0.96613765 + vae.decoder 0.00017875 0.03987194 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 1.70870825 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 176848 +BPFP 0.6257 bits/point +EBPFP 1.2515 equivalent bits/point +MSE 1.708708 +---------------------- -------------------------------------------------------- +Time: 3.760s Load: 0.008s, Pack+Encode: 2.155s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7087 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 936B, BPFP=9.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,672B, BPFP=1.3084 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,392B, BPFP=8.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,852B, BPFP=1.3679 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,584B, BPFP=1.5621 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,552B, BPFP=0.4509 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,544B, BPFP=0.4508 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,600B, BPFP=0.4456 +⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.65797242 + text_encoder-item0.clip_prompt_embeds 0.00027120 85.18218175 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.66230478 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.11071969 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00397441 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00781570 1.61590695 + vae.encoder_f1 0.00781878 1.61527944 + vae.decoder 0.00029724 0.04718096 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 3.61532785 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210784 +BPFP 0.7458 bits/point +EBPFP 1.4916 equivalent bits/point +MSE 3.615328 +---------------------- -------------------------------------------------------- +Time: 3.766s Load: 0.009s, Pack+Encode: 2.154s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6153 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 956B, BPFP=9.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,340B, BPFP=0.9930 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,936B, BPFP=1.0500 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,032B, BPFP=1.1422 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,668B, BPFP=0.4222 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,680B, BPFP=0.4224 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,652B, BPFP=0.5997 +⌛️ [2/4] FRONTEND: Frontend time: 2.177s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.65813331 + text_encoder-item0.clip_prompt_embeds 0.00022930 23.79744360 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.61824722 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.10297375 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00580802 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00577752 0.99192631 + vae.encoder_f1 0.00578475 0.98955703 + vae.decoder 0.00024190 0.05141617 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 1.72041475 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 189248 +BPFP 0.6696 bits/point +EBPFP 1.3392 equivalent bits/point +MSE 1.720415 +---------------------- -------------------------------------------------------- +Time: 3.778s Load: 0.009s, Pack+Encode: 2.177s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7204 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 896B, BPFP=9.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,484B, BPFP=1.1477 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,444B, BPFP=9.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,688B, BPFP=1.1110 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,980B, BPFP=1.2678 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,028B, BPFP=0.4582 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,028B, BPFP=0.4582 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,756B, BPFP=0.2062 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.73456216 + text_encoder-item0.clip_prompt_embeds 0.00028764 23.92104640 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.71984234 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.15518102 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00360648 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.03343784 2.13897467 + vae.encoder_f1 0.03344063 2.13777375 + vae.decoder 0.00016139 0.02218610 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 2.25454613 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 187956 +BPFP 0.6650 bits/point +EBPFP 1.3301 equivalent bits/point +MSE 2.254546 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.009s, Pack+Encode: 2.150s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2545 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 928B, BPFP=9.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,116B, BPFP=0.8274 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,360B, BPFP=8.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,896B, BPFP=0.9656 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,924B, BPFP=1.0634 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,972B, BPFP=0.5184 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,968B, BPFP=0.5183 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,664B, BPFP=0.5085 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.61494593 + text_encoder-item0.clip_prompt_embeds 0.00023094 48.60075927 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.66948729 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.09317690 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00529229 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00637455 1.39650321 + vae.encoder_f1 0.00637988 1.39765501 + vae.decoder 0.00020059 0.04532181 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 2.55639787 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 193480 +BPFP 0.6846 bits/point +EBPFP 1.3692 equivalent bits/point +MSE 2.556398 +---------------------- -------------------------------------------------------- +Time: 3.739s Load: 0.009s, Pack+Encode: 2.137s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5564 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 940B, BPFP=9.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,428B, BPFP=1.0049 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,456B, BPFP=9.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,700B, BPFP=1.0308 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,956B, BPFP=1.1911 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,784B, BPFP=0.4087 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,780B, BPFP=0.4086 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,372B, BPFP=0.4996 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.71041099 + text_encoder-item0.clip_prompt_embeds 0.00025217 23.89483775 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.75128026 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.09616497 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00405337 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00581597 1.02422094 + vae.encoder_f1 0.00582356 1.02380788 + vae.decoder 0.00019494 0.04457477 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 1.73715122 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 186068 +BPFP 0.6584 bits/point +EBPFP 1.3167 equivalent bits/point +MSE 1.737151 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.009s, Pack+Encode: 2.143s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7372 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 892B, BPFP=9.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,176B, BPFP=0.8355 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,404B, BPFP=8.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,248B, BPFP=0.9130 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,492B, BPFP=0.9764 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,036B, BPFP=0.2294 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,044B, BPFP=0.2296 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,116B, BPFP=0.3392 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.71483739 + text_encoder-item0.clip_prompt_embeds 0.00026975 23.71793408 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.68614054 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.08572253 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00408075 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 1.11695218 3.58889318 + vae.encoder_f1 1.11695278 3.58035803 + vae.decoder 0.00019720 0.03823071 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 2.91883186 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 146060 +BPFP 0.5168 bits/point +EBPFP 1.0336 equivalent bits/point +MSE 2.918832 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9188 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 956B, BPFP=9.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,532B, BPFP=1.0189 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,360B, BPFP=8.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,440B, BPFP=1.0097 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,552B, BPFP=1.2315 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,760B, BPFP=0.4083 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,748B, BPFP=0.4081 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,804B, BPFP=0.4213 +⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.64583564 + text_encoder-item0.clip_prompt_embeds 0.00025545 23.91006324 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.60944915 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.09020134 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00399785 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.01535016 1.69566870 + vae.encoder_f1 0.01535382 1.69771230 + vae.decoder 0.00021460 0.04082815 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 2.04871084 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 184804 +BPFP 0.6539 bits/point +EBPFP 1.3078 equivalent bits/point +MSE 2.048711 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.008s, Pack+Encode: 2.155s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0487 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 952B, BPFP=9.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,892B, BPFP=1.0676 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,388B, BPFP=8.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,468B, BPFP=1.0932 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,444B, BPFP=1.2034 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,728B, BPFP=0.3468 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,736B, BPFP=0.3469 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,220B, BPFP=0.5865 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.69042460 + text_encoder-item0.clip_prompt_embeds 0.00022628 23.79326468 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.71729264 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.14243233 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00436880 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00589589 0.90522832 + vae.encoder_f1 0.00590398 0.90256602 + vae.decoder 0.00017838 0.06122297 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 1.68275310 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 182480 +BPFP 0.6457 bits/point +EBPFP 1.2913 equivalent bits/point +MSE 1.682753 +---------------------- -------------------------------------------------------- +Time: 3.747s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6828 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 984B, BPFP=10.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,376B, BPFP=0.9978 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,400B, BPFP=8.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,240B, BPFP=0.9935 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,988B, BPFP=1.0650 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,520B, BPFP=0.4962 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,520B, BPFP=0.4962 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,428B, BPFP=0.2877 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.61545281 + text_encoder-item0.clip_prompt_embeds 0.00031548 23.89264577 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.69947538 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.09983357 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00423683 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00725484 1.74299181 + vae.encoder_f1 0.00725992 1.74266171 + vae.decoder 0.00019960 0.03284618 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 2.06922023 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 185108 +BPFP 0.6550 bits/point +EBPFP 1.3099 equivalent bits/point +MSE 2.069220 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0692 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 924B, BPFP=9.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,364B, BPFP=0.9962 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,504B, BPFP=9.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,380B, BPFP=0.9237 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 36,004B, BPFP=0.9133 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,776B, BPFP=0.4238 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,780B, BPFP=0.4239 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,284B, BPFP=0.3138 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.65616365 + text_encoder-item0.clip_prompt_embeds 0.00021831 23.83789697 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 0.79329748 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.09124398 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00329969 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00923516 1.50512969 + vae.encoder_f1 0.00923823 1.50415933 + vae.decoder 0.00019521 0.02708894 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 1.95622123 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 169668 +BPFP 0.6003 bits/point +EBPFP 1.2007 equivalent bits/point +MSE 1.956221 +---------------------- -------------------------------------------------------- +Time: 3.747s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9562 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 920B, BPFP=9.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,688B, BPFP=0.9048 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,360B, BPFP=8.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,780B, BPFP=1.0373 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,612B, BPFP=1.1570 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,292B, BPFP=0.4470 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,300B, BPFP=0.4471 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,092B, BPFP=0.3690 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.65974601 + text_encoder-item0.clip_prompt_embeds 0.00062166 48.10262784 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.67586956 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.11049724 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00299169 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00831779 1.59039521 + vae.encoder_f1 0.00832197 1.58933449 + vae.decoder 0.00023271 0.03517136 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 2.63205325 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 184696 +BPFP 0.6535 bits/point +EBPFP 1.3070 equivalent bits/point +MSE 2.632053 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6321 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 944B, BPFP=9.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,040B, BPFP=0.9524 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,336B, BPFP=8.3500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,360B, BPFP=1.0032 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,076B, BPFP=1.1941 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,960B, BPFP=0.3656 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,964B, BPFP=0.3657 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,924B, BPFP=0.3334 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.67677323 + text_encoder-item0.clip_prompt_embeds 0.00022938 23.84550232 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.67909074 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.10999115 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00259534 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00626977 1.17579436 + vae.encoder_f1 0.00627489 1.17612159 + vae.decoder 0.00017842 0.04001405 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 1.80614569 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 174256 +BPFP 0.6166 bits/point +EBPFP 1.2331 equivalent bits/point +MSE 1.806146 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8061 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 900B, BPFP=9.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,276B, BPFP=0.9843 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,396B, BPFP=8.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,904B, BPFP=1.0474 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,928B, BPFP=1.0889 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,008B, BPFP=0.3969 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,996B, BPFP=0.3967 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,960B, BPFP=0.5481 +⌛️ [2/4] FRONTEND: Frontend time: 2.156s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.61228963 + text_encoder-item0.clip_prompt_embeds 0.00022180 23.85117779 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.64305816 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.09875563 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00269208 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00585720 1.07401466 + vae.encoder_f1 0.00586586 1.07300699 + vae.decoder 0.00016520 0.05388708 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 1.75987229 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 182020 +BPFP 0.6440 bits/point +EBPFP 1.2881 equivalent bits/point +MSE 1.759872 +---------------------- -------------------------------------------------------- +Time: 3.777s Load: 0.008s, Pack+Encode: 2.156s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7599 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 892B, BPFP=9.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,628B, BPFP=0.8966 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,308B, BPFP=8.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,180B, BPFP=0.9886 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,864B, BPFP=1.1634 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,296B, BPFP=0.3097 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,296B, BPFP=0.3097 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,916B, BPFP=0.3026 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.69609539 + text_encoder-item0.clip_prompt_embeds 0.00025784 23.84908939 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.56950641 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.09929968 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00302187 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00734802 1.30959404 + vae.encoder_f1 0.00734987 1.30927515 + vae.decoder 0.00018093 0.02399436 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 1.86582232 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 164032 +BPFP 0.5804 bits/point +EBPFP 1.1608 equivalent bits/point +MSE 1.865822 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8658 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 892B, BPFP=9.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,980B, BPFP=1.0795 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,376B, BPFP=8.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,748B, BPFP=1.0347 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,440B, BPFP=1.1019 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,940B, BPFP=0.5026 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,924B, BPFP=0.5024 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,864B, BPFP=0.4231 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.70485942 + text_encoder-item0.clip_prompt_embeds 0.00023510 60.97103710 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.64425297 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.09931994 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00274501 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00637359 1.43430281 + vae.encoder_f1 0.00637830 1.43437755 + vae.decoder 0.00018566 0.04440116 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 2.89704361 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 192816 +BPFP 0.6822 bits/point +EBPFP 1.3645 equivalent bits/point +MSE 2.897044 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8970 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 928B, BPFP=9.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,976B, BPFP=0.9437 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,408B, BPFP=8.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,628B, BPFP=1.0250 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,676B, BPFP=1.1586 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,756B, BPFP=0.3625 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,740B, BPFP=0.3622 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,644B, BPFP=0.2638 +⌛️ [2/4] FRONTEND: Frontend time: 2.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.65960836 + text_encoder-item0.clip_prompt_embeds 0.00026418 23.84076747 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.66550431 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.10107629 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00378671 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.01530954 1.58991623 + vae.encoder_f1 0.01531230 1.58758521 + vae.decoder 0.00017892 0.02749486 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 1.99577451 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170408 +BPFP 0.6029 bits/point +EBPFP 1.2059 equivalent bits/point +MSE 1.995775 +---------------------- -------------------------------------------------------- +Time: 3.731s Load: 0.009s, Pack+Encode: 2.132s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9958 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 936B, BPFP=9.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,820B, BPFP=1.0579 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,364B, BPFP=8.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,288B, BPFP=1.0786 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,000B, BPFP=1.1668 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,116B, BPFP=0.4443 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,100B, BPFP=0.4440 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,424B, BPFP=0.4402 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.60395086 + text_encoder-item0.clip_prompt_embeds 0.00021481 35.87304688 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.62506914 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.09911778 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00379784 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00591154 1.49912059 + vae.encoder_f1 0.00591973 1.49968040 + vae.decoder 0.00025286 0.04313998 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 2.27072800 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188700 +BPFP 0.6677 bits/point +EBPFP 1.3353 equivalent bits/point +MSE 2.270728 +---------------------- -------------------------------------------------------- +Time: 3.726s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2707 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 936B, BPFP=9.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,300B, BPFP=1.1228 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,320B, BPFP=8.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,212B, BPFP=1.1536 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,320B, BPFP=1.2510 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,800B, BPFP=0.2869 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,796B, BPFP=0.2868 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,096B, BPFP=0.4912 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.66638144 + text_encoder-item0.clip_prompt_embeds 0.00023458 23.82641919 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.71883759 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.10870324 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00418031 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00588703 0.79691720 + vae.encoder_f1 0.00589573 0.79678160 + vae.decoder 0.00053402 0.04706987 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 1.63083009 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 174432 +BPFP 0.6172 bits/point +EBPFP 1.2344 equivalent bits/point +MSE 1.630830 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6308 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 888B, BPFP=9.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,772B, BPFP=1.0514 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,404B, BPFP=8.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,968B, BPFP=1.0526 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,076B, BPFP=1.1687 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,720B, BPFP=0.4230 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,708B, BPFP=0.4228 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,748B, BPFP=0.2975 +⌛️ [2/4] FRONTEND: Frontend time: 2.160s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.64530953 + text_encoder-item0.clip_prompt_embeds 0.00022882 23.62842431 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.67727494 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.09831466 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00350709 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00659691 1.38663971 + vae.encoder_f1 0.00660300 1.39126611 + vae.decoder 0.00023739 0.03080056 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 1.89778653 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 180936 +BPFP 0.6402 bits/point +EBPFP 1.2804 equivalent bits/point +MSE 1.897787 +---------------------- -------------------------------------------------------- +Time: 3.768s Load: 0.009s, Pack+Encode: 2.160s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8978 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 868B, BPFP=9.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,196B, BPFP=0.9735 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,360B, BPFP=8.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,528B, BPFP=1.0169 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,108B, BPFP=1.0934 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,948B, BPFP=0.3196 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,932B, BPFP=0.3194 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,980B, BPFP=0.5792 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.68969297 + text_encoder-item0.clip_prompt_embeds 0.00023928 23.85797146 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.69536738 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.09753203 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00376788 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00583864 0.85121435 + vae.encoder_f1 0.00583800 0.85201919 + vae.decoder 0.00018889 0.05308478 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 1.65720220 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 172572 +BPFP 0.6106 bits/point +EBPFP 1.2212 equivalent bits/point +MSE 1.657202 +---------------------- -------------------------------------------------------- +Time: 3.762s Load: 0.009s, Pack+Encode: 2.152s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6572 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 912B, BPFP=9.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,772B, BPFP=1.0514 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,396B, BPFP=8.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,240B, BPFP=1.0747 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,168B, BPFP=1.2472 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,832B, BPFP=0.2568 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,832B, BPFP=0.2568 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,008B, BPFP=0.4580 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.585s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.68176627 + text_encoder-item0.clip_prompt_embeds 0.00024821 23.82921782 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.53829937 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.10042224 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00343600 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00570467 0.73056966 + vae.encoder_f1 0.00570488 0.73097777 + vae.decoder 0.00017302 0.04352186 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 1.59928634 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 167812 +BPFP 0.5938 bits/point +EBPFP 1.1875 equivalent bits/point +MSE 1.599286 +---------------------- -------------------------------------------------------- +Time: 3.745s Load: 0.009s, Pack+Encode: 2.151s, Decode+Unpack: 1.585s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.5993 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 920B, BPFP=9.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,736B, BPFP=1.0465 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,368B, BPFP=8.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,500B, BPFP=1.0958 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,752B, BPFP=1.1859 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,328B, BPFP=0.2491 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,336B, BPFP=0.2493 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,504B, BPFP=0.2290 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.66725596 + text_encoder-item0.clip_prompt_embeds 0.00021458 132.66051136 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.58836441 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.11094986 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00397130 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00914783 1.43407273 + vae.encoder_f1 0.00914958 1.43265986 + vae.decoder 0.00017527 0.02632149 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 4.77015978 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 157096 +BPFP 0.5558 bits/point +EBPFP 1.1117 equivalent bits/point +MSE 4.770160 +---------------------- -------------------------------------------------------- +Time: 3.773s Load: 0.007s, Pack+Encode: 2.161s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7702 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 920B, BPFP=9.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,464B, BPFP=1.1450 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,428B, BPFP=8.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,424B, BPFP=1.1708 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,368B, BPFP=1.2522 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,148B, BPFP=0.3227 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,152B, BPFP=0.3228 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,192B, BPFP=0.5857 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.66428010 + text_encoder-item0.clip_prompt_embeds 0.00022150 23.87209568 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.70738764 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.13863264 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00367300 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00578482 0.82803875 + vae.encoder_f1 0.00579739 0.82766569 + vae.decoder 0.00017668 0.05613483 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 1.64868058 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 182748 +BPFP 0.6466 bits/point +EBPFP 1.2932 equivalent bits/point +MSE 1.648681 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.009s, Pack+Encode: 2.149s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6487 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 888B, BPFP=9.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,136B, BPFP=0.9654 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,368B, BPFP=8.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,188B, BPFP=0.9893 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,612B, BPFP=1.0809 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,960B, BPFP=0.3198 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,956B, BPFP=0.3198 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,464B, BPFP=0.4109 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.69087338 + text_encoder-item0.clip_prompt_embeds 0.00023894 23.76742805 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.69461684 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.09887407 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00549400 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00958025 1.27460873 + vae.encoder_f1 0.00958229 1.27666354 + vae.decoder 0.00019995 0.04265805 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 1.85057108 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 166224 +BPFP 0.5881 bits/point +EBPFP 1.1763 equivalent bits/point +MSE 1.850571 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8506 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 916B, BPFP=9.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,552B, BPFP=1.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,416B, BPFP=8.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,760B, BPFP=1.1981 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,012B, BPFP=1.2432 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,852B, BPFP=0.2419 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,844B, BPFP=0.2418 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,496B, BPFP=0.5950 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.65800269 + text_encoder-item0.clip_prompt_embeds 0.00023387 108.62542275 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.65403123 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.10779743 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00383281 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00567713 0.75500381 + vae.encoder_f1 0.00567905 0.75495392 + vae.decoder 0.00019376 0.06130229 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 3.83084323 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 172500 +BPFP 0.6104 bits/point +EBPFP 1.2207 equivalent bits/point +MSE 3.830843 +---------------------- -------------------------------------------------------- +Time: 3.731s Load: 0.009s, Pack+Encode: 2.134s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8308 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 920B, BPFP=9.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,408B, BPFP=1.0022 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,372B, BPFP=8.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,348B, BPFP=1.0023 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,152B, BPFP=1.2468 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,364B, BPFP=0.3260 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,360B, BPFP=0.3259 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,092B, BPFP=0.3690 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.62689674 + text_encoder-item0.clip_prompt_embeds 0.00024281 23.81977772 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.64414978 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.09737874 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00328313 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.02387581 1.24992442 + vae.encoder_f1 0.02387858 1.25036860 + vae.decoder 0.00018648 0.02961794 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 1.83818322 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 172668 +BPFP 0.6109 bits/point +EBPFP 1.2219 equivalent bits/point +MSE 1.838183 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8382 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 948B, BPFP=9.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,884B, BPFP=1.0666 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,368B, BPFP=8.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,684B, BPFP=1.0295 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,668B, BPFP=1.1330 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,280B, BPFP=0.4926 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,288B, BPFP=0.4927 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,620B, BPFP=0.2631 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.66359663 + text_encoder-item0.clip_prompt_embeds 0.00022399 23.80881781 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.63814483 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.10178114 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00404768 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.01169517 1.89496183 + vae.encoder_f1 0.01169969 1.89701414 + vae.decoder 0.00021186 0.02662977 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 2.13737842 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 187392 +BPFP 0.6630 bits/point +EBPFP 1.3261 equivalent bits/point +MSE 2.137378 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1374 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 940B, BPFP=9.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,376B, BPFP=0.9978 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,408B, BPFP=8.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,612B, BPFP=1.0237 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,544B, BPFP=1.3074 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,040B, BPFP=0.3973 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,040B, BPFP=0.3973 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,936B, BPFP=0.4863 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.63369028 + text_encoder-item0.clip_prompt_embeds 0.00022123 252.55806953 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.64604659 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.09622067 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00397360 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.32749966 3.37834024 + vae.encoder_f1 0.32750070 3.38010597 + vae.decoder 0.00039956 0.03999647 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 8.80945874 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188548 +BPFP 0.6671 bits/point +EBPFP 1.3343 equivalent bits/point +MSE 8.809459 +---------------------- -------------------------------------------------------- +Time: 3.727s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.8095 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 892B, BPFP=9.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,500B, BPFP=1.0146 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,040B, BPFP=1.0584 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,952B, BPFP=1.1656 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,532B, BPFP=0.3286 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,536B, BPFP=0.3286 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,164B, BPFP=0.4628 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.65164415 + text_encoder-item0.clip_prompt_embeds 0.00024675 23.91254904 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.64982710 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.10146213 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00467080 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00566967 0.90382814 + vae.encoder_f1 0.00567867 0.90217358 + vae.decoder 0.00017839 0.04174539 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 1.68140383 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 173600 +BPFP 0.6142 bits/point +EBPFP 1.2285 equivalent bits/point +MSE 1.681404 +---------------------- -------------------------------------------------------- +Time: 3.761s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.610s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6814 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 956B, BPFP=9.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 5,776B, BPFP=0.7814 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,420B, BPFP=8.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 11,796B, BPFP=0.9575 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,076B, BPFP=1.1434 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,572B, BPFP=0.2986 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,572B, BPFP=0.2986 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,380B, BPFP=0.6219 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.58303412 + text_encoder-item0.clip_prompt_embeds 0.00022364 35.74513833 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.73890829 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.10110905 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00302155 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00580750 0.81985664 + vae.encoder_f1 0.00580664 0.81985152 + vae.decoder 0.00018044 0.06294113 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 1.95456224 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 171200 +BPFP 0.6058 bits/point +EBPFP 1.2115 equivalent bits/point +MSE 1.954562 +---------------------- -------------------------------------------------------- +Time: 3.747s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9546 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 916B, BPFP=9.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,904B, BPFP=0.9340 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,348B, BPFP=8.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,364B, BPFP=1.0036 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,176B, BPFP=1.0698 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,756B, BPFP=0.3472 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,756B, BPFP=0.3472 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,344B, BPFP=0.3157 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.65655899 + text_encoder-item0.clip_prompt_embeds 0.00030118 240.76170184 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.62395878 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.09475248 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00343672 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.03869025 1.70287752 + vae.encoder_f1 0.03869358 1.70683849 + vae.decoder 0.00021614 0.03497456 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 7.72368300 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 166216 +BPFP 0.5881 bits/point +EBPFP 1.1762 equivalent bits/point +MSE 7.723683 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.7237 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 932B, BPFP=9.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,316B, BPFP=0.9897 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,436B, BPFP=8.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,168B, BPFP=0.9877 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,696B, BPFP=1.1084 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,376B, BPFP=0.4940 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,388B, BPFP=0.4942 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,936B, BPFP=0.2727 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.66621963 + text_encoder-item0.clip_prompt_embeds 0.00023260 36.22393720 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.71412706 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.09944739 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00346631 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00839879 1.63616705 + vae.encoder_f1 0.00840224 1.63326669 + vae.decoder 0.00019463 0.03304851 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 2.34153058 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 185900 +BPFP 0.6578 bits/point +EBPFP 1.3155 equivalent bits/point +MSE 2.341531 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3415 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 912B, BPFP=9.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,256B, BPFP=1.1169 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,456B, BPFP=9.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,180B, BPFP=1.1510 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,572B, BPFP=1.1813 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,424B, BPFP=0.4490 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,420B, BPFP=0.4489 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,080B, BPFP=0.3076 +⌛️ [2/4] FRONTEND: Frontend time: 2.131s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.583s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.63923041 + text_encoder-item0.clip_prompt_embeds 0.00023544 23.90605553 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.67538109 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.10784285 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00425431 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.01160815 1.68913531 + vae.encoder_f1 0.01161249 1.68862820 + vae.decoder 0.00021720 0.03016318 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 2.04458793 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 186952 +BPFP 0.6615 bits/point +EBPFP 1.3230 equivalent bits/point +MSE 2.044588 +---------------------- -------------------------------------------------------- +Time: 3.723s Load: 0.009s, Pack+Encode: 2.131s, Decode+Unpack: 1.583s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0446 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 948B, BPFP=9.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,636B, BPFP=1.0330 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,432B, BPFP=8.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,756B, BPFP=1.0354 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,548B, BPFP=1.1300 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,360B, BPFP=0.2802 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,360B, BPFP=0.2802 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,880B, BPFP=0.2710 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.65232468 + text_encoder-item0.clip_prompt_embeds 0.00022923 23.90198652 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.66018562 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.09751957 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00345611 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.02989292 1.79978859 + vae.encoder_f1 0.02989391 1.80073118 + vae.decoder 0.00034944 0.02785286 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 2.09530176 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 159572 +BPFP 0.5646 bits/point +EBPFP 1.1292 equivalent bits/point +MSE 2.095302 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0953 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 904B, BPFP=9.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,844B, BPFP=1.0611 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,456B, BPFP=9.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,656B, BPFP=1.0273 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,116B, BPFP=1.1444 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,076B, BPFP=11.2083 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,336B, BPFP=0.7219 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 9,828B, BPFP=0.7977 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 28,660B, BPFP=0.7270 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,304B, BPFP=0.4624 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,312B, BPFP=0.4625 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,656B, BPFP=0.4167 +⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.585s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.70624709 + text_encoder-item0.clip_prompt_embeds 0.00024627 23.81140084 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.71398273 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.09456719 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00355713 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.69240173 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.73548473 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.41694841 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11957858 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00146340 + vae.encoder_f0 0.00613025 1.19323599 + vae.encoder_f1 0.00613536 1.19431138 + vae.decoder 0.00018697 0.04138908 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 1.81316712 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188900 +BPFP 0.6684 bits/point +EBPFP 1.3368 equivalent bits/point +MSE 1.813167 +---------------------- -------------------------------------------------------- +Time: 3.729s Load: 0.008s, Pack+Encode: 2.136s, Decode+Unpack: 1.585s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8132 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.007/elic-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.6247 bits/point +Avg EBPFP 1.2493 equivalent bits/point +Avg MSE 2.410230 +Avg Time 3.767s +------------------------ ---------------------------- diff --git a/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log b/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..a92bfd1c575fb49de48f4b57813667ae5604dd2d --- /dev/null +++ b/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log @@ -0,0 +1,21862 @@ +Experiment: dtufc_hyperprior-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: sd35 + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 599 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 752B, BPFP=7.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,604B, BPFP=1.1640 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,100B, BPFP=1.1445 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,332B, BPFP=1.1752 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,316B, BPFP=0.4778 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,316B, BPFP=0.4778 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,764B, BPFP=0.4200 +⌛️ [2/4] FRONTEND: Frontend time: 0.686s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.93479474 + text_encoder-item0.clip_prompt_embeds 0.00025464 23.88423295 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.83030148 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.11002125 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00238927 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00635250 1.48675561 + vae.encoder_f1 0.00635834 1.48672032 + vae.decoder 0.00019940 0.04112590 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 1.95100340 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 197492 +BPFP 0.6988 bits/point +EBPFP 1.3976 equivalent bits/point +MSE 1.951003 +---------------------- -------------------------------------------------------- +Time: 1.199s Load: 0.009s, Pack+Encode: 0.686s, Decode+Unpack: 0.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9510 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 780B, BPFP=8.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,056B, BPFP=1.0898 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,412B, BPFP=1.1698 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,316B, BPFP=1.1241 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,244B, BPFP=0.3547 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,244B, BPFP=0.3547 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,116B, BPFP=0.3392 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.86179630 + text_encoder-item0.clip_prompt_embeds 0.00022609 23.88390532 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.88897686 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.11024687 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00236742 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.01130640 2.02853036 + vae.encoder_f1 0.01130902 2.02933979 + vae.decoder 0.00020860 0.04012117 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 2.20234729 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 176464 +BPFP 0.6244 bits/point +EBPFP 1.2488 equivalent bits/point +MSE 2.202347 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2023 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,752B, BPFP=1.0487 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,284B, BPFP=8.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,504B, BPFP=1.0961 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,896B, BPFP=1.0373 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,864B, BPFP=0.1963 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,864B, BPFP=0.1963 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,168B, BPFP=0.2493 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.90364011 + text_encoder-item0.clip_prompt_embeds 0.00022402 23.88596836 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 1.07118244 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.09820207 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00177182 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 1.19630027 6.22988367 + vae.encoder_f1 1.19630098 6.22748899 + vae.decoder 0.00023596 0.03400736 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 4.14891236 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 148160 +BPFP 0.5242 bits/point +EBPFP 1.0485 equivalent bits/point +MSE 4.148912 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.007s, Pack+Encode: 0.287s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1489 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,968B, BPFP=1.0779 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,276B, BPFP=7.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,424B, BPFP=1.1708 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,356B, BPFP=1.0997 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,872B, BPFP=0.3948 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,868B, BPFP=0.3947 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,620B, BPFP=0.5682 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.90406386 + text_encoder-item0.clip_prompt_embeds 0.00030342 23.89041151 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.85540295 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.10573070 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00204047 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00586287 1.19374382 + vae.encoder_f1 0.00587438 1.19393659 + vae.decoder 0.00017677 0.06110790 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 1.81741320 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188196 +BPFP 0.6659 bits/point +EBPFP 1.3318 equivalent bits/point +MSE 1.817413 +---------------------- -------------------------------------------------------- +Time: 0.736s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8174 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,452B, BPFP=1.0081 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,364B, BPFP=1.0847 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,808B, BPFP=0.9844 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,600B, BPFP=0.3143 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,600B, BPFP=0.3143 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,468B, BPFP=0.3195 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.87077212 + text_encoder-item0.clip_prompt_embeds 0.00024120 23.89084483 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.95850182 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.09769281 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00173625 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00779453 1.51460063 + vae.encoder_f1 0.00779802 1.51466346 + vae.decoder 0.00023829 0.03809280 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 1.96318338 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 163372 +BPFP 0.5781 bits/point +EBPFP 1.1561 equivalent bits/point +MSE 1.963183 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9632 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,112B, BPFP=1.0974 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,636B, BPFP=1.1068 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,312B, BPFP=1.0986 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,604B, BPFP=0.4670 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,604B, BPFP=0.4670 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,572B, BPFP=0.3531 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.88422497 + text_encoder-item0.clip_prompt_embeds 0.00025651 23.89174953 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.91841822 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.09878363 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00214347 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00655775 1.67202783 + vae.encoder_f1 0.00656268 1.67191243 + vae.decoder 0.00020283 0.04144643 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 2.03665049 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 189920 +BPFP 0.6720 bits/point +EBPFP 1.3440 equivalent bits/point +MSE 2.036650 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.008s, Pack+Encode: 0.287s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0367 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,124B, BPFP=0.9637 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,276B, BPFP=0.9964 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 37,516B, BPFP=0.9516 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,424B, BPFP=0.4337 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,424B, BPFP=0.4337 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,984B, BPFP=0.4573 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.92069697 + text_encoder-item0.clip_prompt_embeds 0.00022242 23.86694653 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.91829796 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.09119296 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00188271 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00593415 1.32071352 + vae.encoder_f1 0.00594307 1.32051837 + vae.decoder 0.00018992 0.05561148 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 1.87434228 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 180812 +BPFP 0.6398 bits/point +EBPFP 1.2795 equivalent bits/point +MSE 1.874342 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8743 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 756B, BPFP=7.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,932B, BPFP=1.0731 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,276B, BPFP=1.1588 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,000B, BPFP=1.0400 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,952B, BPFP=0.4113 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,952B, BPFP=0.4113 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,852B, BPFP=0.3617 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.88733951 + text_encoder-item0.clip_prompt_embeds 0.00022110 23.88472335 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.89068174 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.11944847 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00218531 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00641770 1.40176773 + vae.encoder_f1 0.00642053 1.40159976 + vae.decoder 0.00017498 0.03377140 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 1.91111866 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 181012 +BPFP 0.6405 bits/point +EBPFP 1.2809 equivalent bits/point +MSE 1.911119 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9111 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,028B, BPFP=0.9508 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,252B, BPFP=7.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,428B, BPFP=1.0899 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,664B, BPFP=1.1075 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,884B, BPFP=0.3339 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,884B, BPFP=0.3339 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,164B, BPFP=0.4628 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.89223329 + text_encoder-item0.clip_prompt_embeds 0.00021654 23.88157806 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.95828304 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.09912693 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00212330 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00577698 1.16771090 + vae.encoder_f1 0.00578348 1.16759050 + vae.decoder 0.00017559 0.04907284 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 1.80341884 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 175116 +BPFP 0.6196 bits/point +EBPFP 1.2392 equivalent bits/point +MSE 1.803419 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8034 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,472B, BPFP=1.0108 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,796B, BPFP=1.1198 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,536B, BPFP=1.1297 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,884B, BPFP=0.3950 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,884B, BPFP=0.3950 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,256B, BPFP=0.3130 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.89966750 + text_encoder-item0.clip_prompt_embeds 0.00022160 23.86980858 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.80633297 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.09592828 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00216250 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00668450 1.35820448 + vae.encoder_f1 0.00668875 1.35763764 + vae.decoder 0.00023059 0.04227874 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 1.89034716 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 179912 +BPFP 0.6366 bits/point +EBPFP 1.2732 equivalent bits/point +MSE 1.890347 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8903 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,900B, BPFP=1.0687 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,224B, BPFP=7.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,784B, BPFP=1.1188 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,604B, BPFP=1.1314 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,404B, BPFP=0.3571 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,408B, BPFP=0.3572 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,500B, BPFP=0.2289 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.88812184 + text_encoder-item0.clip_prompt_embeds 0.00023190 23.86673304 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.76850910 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.10436709 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00222842 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.04018118 2.40489411 + vae.encoder_f1 0.04018488 2.40323448 + vae.decoder 0.00016201 0.02682562 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 2.37399465 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 172648 +BPFP 0.6109 bits/point +EBPFP 1.2218 equivalent bits/point +MSE 2.373995 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3740 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 756B, BPFP=7.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,712B, BPFP=1.0433 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,260B, BPFP=7.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,208B, BPFP=1.0721 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,172B, BPFP=1.0697 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,116B, BPFP=0.4138 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,116B, BPFP=0.4138 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,572B, BPFP=0.3837 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.93257165 + text_encoder-item0.clip_prompt_embeds 0.00023140 23.88209170 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.84595118 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.09310864 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00211820 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.04874706 2.19654107 + vae.encoder_f1 0.04875064 2.19756246 + vae.decoder 0.00019641 0.03496123 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 2.27888659 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 181964 +BPFP 0.6438 bits/point +EBPFP 1.2877 equivalent bits/point +MSE 2.278887 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2789 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,948B, BPFP=1.0752 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,252B, BPFP=7.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,992B, BPFP=1.1357 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,376B, BPFP=1.0241 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,680B, BPFP=0.4987 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,680B, BPFP=0.4987 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,176B, BPFP=0.2190 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.88966409 + text_encoder-item0.clip_prompt_embeds 0.00030893 23.87838838 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.85796909 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.09751372 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00184390 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.01360236 2.19832730 + vae.encoder_f1 0.01360807 2.19800520 + vae.decoder 0.00023006 0.03229189 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 2.27914310 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 186916 +BPFP 0.6614 bits/point +EBPFP 1.3227 equivalent bits/point +MSE 2.279143 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2791 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,076B, BPFP=1.0925 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,272B, BPFP=7.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,640B, BPFP=1.1071 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,372B, BPFP=1.1509 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,048B, BPFP=0.1838 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,048B, BPFP=0.1838 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 4,340B, BPFP=0.1324 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.91362000 + text_encoder-item0.clip_prompt_embeds 0.00024198 23.88085303 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.89404707 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.10876006 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00242008 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 1.67190456 5.38228226 + vae.encoder_f1 1.67190480 5.38165188 + vae.decoder 0.00017417 0.01664760 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 3.75453822 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 147620 +BPFP 0.5223 bits/point +EBPFP 1.0446 equivalent bits/point +MSE 3.754538 +---------------------- -------------------------------------------------------- +Time: 0.736s Load: 0.007s, Pack+Encode: 0.286s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7545 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 752B, BPFP=7.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,812B, BPFP=1.0568 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,236B, BPFP=7.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,256B, BPFP=1.0760 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,632B, BPFP=1.1575 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,208B, BPFP=0.4915 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,208B, BPFP=0.4915 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,084B, BPFP=0.3993 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.87722683 + text_encoder-item0.clip_prompt_embeds 0.00025129 23.88707175 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.88144159 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.10599080 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00242299 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00621760 1.47214198 + vae.encoder_f1 0.00622505 1.47226250 + vae.decoder 0.00025114 0.04781596 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 1.94495051 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 196240 +BPFP 0.6944 bits/point +EBPFP 1.3887 equivalent bits/point +MSE 1.944951 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9450 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 748B, BPFP=7.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 6,936B, BPFP=0.9383 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,264B, BPFP=7.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,176B, BPFP=1.0695 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,452B, BPFP=1.0007 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,680B, BPFP=0.5292 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,680B, BPFP=0.5292 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,032B, BPFP=0.3977 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.92911235 + text_encoder-item0.clip_prompt_embeds 0.00020838 23.88466628 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.90009270 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.09691031 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00174445 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00675961 1.93189311 + vae.encoder_f1 0.00676652 1.93224621 + vae.decoder 0.00021373 0.05065523 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 2.15802634 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 194020 +BPFP 0.6865 bits/point +EBPFP 1.3730 equivalent bits/point +MSE 2.158026 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1580 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 752B, BPFP=7.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,428B, BPFP=1.0049 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,260B, BPFP=7.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,728B, BPFP=1.1143 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,996B, BPFP=1.1160 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,628B, BPFP=0.2537 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,628B, BPFP=0.2537 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,860B, BPFP=0.7281 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.87631400 + text_encoder-item0.clip_prompt_embeds 0.00021387 23.85997320 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.84112949 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.10559927 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00216654 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00596338 0.93930721 + vae.encoder_f1 0.00596322 0.93919539 + vae.decoder 0.00018207 0.06875762 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 1.69942815 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 174332 +BPFP 0.6168 bits/point +EBPFP 1.2337 equivalent bits/point +MSE 1.699428 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6994 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 780B, BPFP=8.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,404B, BPFP=1.0016 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,248B, BPFP=7.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,380B, BPFP=1.0860 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 37,592B, BPFP=0.9535 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 12,636B, BPFP=0.1928 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 12,636B, BPFP=0.1928 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,340B, BPFP=0.5292 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.84365543 + text_encoder-item0.clip_prompt_embeds 0.00022138 23.86879397 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.86605892 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.10431087 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00195701 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00552804 0.74986732 + vae.encoder_f1 0.00552758 0.74989307 + vae.decoder 0.00018040 0.05642245 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 1.61032204 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 153068 +BPFP 0.5416 bits/point +EBPFP 1.0832 equivalent bits/point +MSE 1.610322 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.009s, Pack+Encode: 0.286s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6103 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,944B, BPFP=1.0747 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,208B, BPFP=1.0721 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,856B, BPFP=1.0617 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,620B, BPFP=0.2689 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,620B, BPFP=0.2689 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,628B, BPFP=0.2938 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.92505900 + text_encoder-item0.clip_prompt_embeds 0.00024507 23.87651980 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.90329504 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.11006475 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00213243 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00721525 1.29185438 + vae.encoder_f1 0.00721777 1.29176629 + vae.decoder 0.00018707 0.03300231 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 1.85946267 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 159956 +BPFP 0.5660 bits/point +EBPFP 1.1319 equivalent bits/point +MSE 1.859463 +---------------------- -------------------------------------------------------- +Time: 0.739s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8595 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 748B, BPFP=7.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,240B, BPFP=0.9794 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,168B, BPFP=0.9877 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 34,468B, BPFP=0.8743 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,160B, BPFP=0.3992 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,156B, BPFP=0.3991 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,360B, BPFP=0.3772 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.89243619 + text_encoder-item0.clip_prompt_embeds 0.00046272 23.89515270 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.96257935 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.09312959 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00169544 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.01999603 2.13359618 + vae.encoder_f1 0.01999529 2.13323450 + vae.decoder 0.00024882 0.04479950 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 2.25085066 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170608 +BPFP 0.6037 bits/point +EBPFP 1.2073 equivalent bits/point +MSE 2.250851 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2509 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,436B, BPFP=1.0060 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,260B, BPFP=7.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,148B, BPFP=1.0672 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 37,516B, BPFP=0.9516 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,916B, BPFP=0.4260 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,916B, BPFP=0.4260 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,324B, BPFP=0.2540 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.87865941 + text_encoder-item0.clip_prompt_embeds 0.00020334 23.88528350 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.81868458 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.09569290 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00170467 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.01341345 2.02705359 + vae.encoder_f1 0.01341645 2.02726626 + vae.decoder 0.00018350 0.02608301 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 2.19917152 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 174340 +BPFP 0.6169 bits/point +EBPFP 1.2337 equivalent bits/point +MSE 2.199172 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1992 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,216B, BPFP=0.9762 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,764B, BPFP=1.0360 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,596B, BPFP=1.1058 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,592B, BPFP=0.4210 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,592B, BPFP=0.4210 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,808B, BPFP=0.5129 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.88459619 + text_encoder-item0.clip_prompt_embeds 0.00022316 71.42608563 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.88334675 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.10432589 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00208208 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00606298 1.28208411 + vae.encoder_f1 0.00607096 1.28211784 + vae.decoder 0.00023408 0.05615795 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 3.10101599 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 187632 +BPFP 0.6639 bits/point +EBPFP 1.3278 equivalent bits/point +MSE 3.101016 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1010 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,388B, BPFP=0.9995 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,264B, BPFP=7.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,788B, BPFP=1.0380 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,712B, BPFP=1.1088 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,828B, BPFP=0.4094 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,824B, BPFP=0.4093 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,140B, BPFP=0.4620 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.88118410 + text_encoder-item0.clip_prompt_embeds 0.00023597 23.88445490 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.89050112 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.09249163 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00189151 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00653100 1.37795579 + vae.encoder_f1 0.00653745 1.37838030 + vae.decoder 0.00020026 0.04717865 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 1.90054200 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 184768 +BPFP 0.6538 bits/point +EBPFP 1.3075 equivalent bits/point +MSE 1.900542 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9005 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,736B, BPFP=1.0465 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,248B, BPFP=7.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,668B, BPFP=1.1094 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,224B, BPFP=1.0457 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,356B, BPFP=0.4174 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,360B, BPFP=0.4175 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,428B, BPFP=0.2877 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.88137356 + text_encoder-item0.clip_prompt_embeds 0.00022433 23.87535089 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.91375113 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.10638368 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00191377 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00869686 2.28428650 + vae.encoder_f1 0.00870063 2.28402328 + vae.decoder 0.00021246 0.03666355 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 2.31987447 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 178840 +BPFP 0.6328 bits/point +EBPFP 1.2656 equivalent bits/point +MSE 2.319874 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3199 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,936B, BPFP=1.0736 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,828B, BPFP=1.1224 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,356B, BPFP=1.0744 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,004B, BPFP=0.4883 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,004B, BPFP=0.4883 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,664B, BPFP=0.3254 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.91107607 + text_encoder-item0.clip_prompt_embeds 0.00022433 23.86825284 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.86351624 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.09716049 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00184673 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00626512 1.68743134 + vae.encoder_f1 0.00626949 1.68749809 + vae.decoder 0.00018936 0.03949942 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 2.04286198 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 190860 +BPFP 0.6753 bits/point +EBPFP 1.3506 equivalent bits/point +MSE 2.042862 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0429 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,956B, BPFP=1.0763 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,008B, BPFP=1.1370 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,928B, BPFP=1.1142 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,256B, BPFP=0.3396 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,256B, BPFP=0.3396 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,156B, BPFP=0.2489 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.95910501 + text_encoder-item0.clip_prompt_embeds 0.00026137 23.87467025 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.84028254 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.10896297 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00212096 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.35915655 3.64306521 + vae.encoder_f1 0.35915723 3.64410424 + vae.decoder 0.00024181 0.03167757 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 2.94986494 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170620 +BPFP 0.6037 bits/point +EBPFP 1.2074 equivalent bits/point +MSE 2.949865 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9499 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,168B, BPFP=0.9697 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,252B, BPFP=7.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,320B, BPFP=1.0000 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 36,996B, BPFP=0.9384 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,100B, BPFP=0.1694 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,100B, BPFP=0.1694 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,628B, BPFP=0.2328 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.92490164 + text_encoder-item0.clip_prompt_embeds 0.00021656 23.87876040 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.90657558 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.09246620 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00181160 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.29031765 2.71727538 + vae.encoder_f1 0.29031771 2.71725678 + vae.decoder 0.00019965 0.04277731 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 2.52092543 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 138376 +BPFP 0.4896 bits/point +EBPFP 0.9792 equivalent bits/point +MSE 2.520925 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5209 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,220B, BPFP=0.9767 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,508B, BPFP=1.0153 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 35,520B, BPFP=0.9010 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,544B, BPFP=0.3287 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,544B, BPFP=0.3287 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,544B, BPFP=0.5964 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.92097092 + text_encoder-item0.clip_prompt_embeds 0.00025451 23.86515194 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.85409689 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.09598514 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00182955 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00595764 1.00973165 + vae.encoder_f1 0.00596395 1.00975478 + vae.decoder 0.00019845 0.06064776 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 1.73087163 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 169952 +BPFP 0.6013 bits/point +EBPFP 1.2027 equivalent bits/point +MSE 1.730872 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7309 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,864B, BPFP=1.0639 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,348B, BPFP=1.1646 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,240B, BPFP=1.1222 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,220B, BPFP=0.2628 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,220B, BPFP=0.2628 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,660B, BPFP=0.2338 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.88380146 + text_encoder-item0.clip_prompt_embeds 0.00026157 23.85924817 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.87855377 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.10897768 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00226880 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.40456498 4.86744976 + vae.encoder_f1 0.40456539 4.86695719 + vae.decoder 0.00020503 0.03142140 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 3.51692459 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 160620 +BPFP 0.5683 bits/point +EBPFP 1.1366 equivalent bits/point +MSE 3.516925 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5169 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,212B, BPFP=1.1109 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,944B, BPFP=1.0506 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 35,700B, BPFP=0.9055 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,836B, BPFP=0.4247 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,836B, BPFP=0.4247 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,008B, BPFP=0.4885 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.87266429 + text_encoder-item0.clip_prompt_embeds 0.00027179 23.86852340 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.91720495 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.09784121 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00180025 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00673531 1.97407675 + vae.encoder_f1 0.00673732 1.97420418 + vae.decoder 0.00020129 0.05324866 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 2.17745478 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 180600 +BPFP 0.6390 bits/point +EBPFP 1.2780 equivalent bits/point +MSE 2.177455 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1775 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,712B, BPFP=1.1786 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,236B, BPFP=7.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,512B, BPFP=1.0968 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,504B, BPFP=1.1542 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,628B, BPFP=0.3605 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,628B, BPFP=0.3605 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,032B, BPFP=0.2451 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.90812087 + text_encoder-item0.clip_prompt_embeds 0.00023057 23.85079520 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.93819942 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.10755307 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00248251 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00881784 1.99924612 + vae.encoder_f1 0.00882136 1.99890804 + vae.decoder 0.00017598 0.02733704 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 2.18609412 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 175072 +BPFP 0.6195 bits/point +EBPFP 1.2389 equivalent bits/point +MSE 2.186094 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1861 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,856B, BPFP=1.0628 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,676B, BPFP=1.1101 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,648B, BPFP=1.0818 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,276B, BPFP=0.3399 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,280B, BPFP=0.3400 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,956B, BPFP=0.4869 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.88002173 + text_encoder-item0.clip_prompt_embeds 0.00025208 23.86466154 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.87883959 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.10891523 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00206820 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00582247 1.04667437 + vae.encoder_f1 0.00582996 1.04646575 + vae.decoder 0.00016099 0.05741904 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 1.74816061 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 176760 +BPFP 0.6254 bits/point +EBPFP 1.2508 equivalent bits/point +MSE 1.748161 +---------------------- -------------------------------------------------------- +Time: 0.739s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7482 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,244B, BPFP=0.9800 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,224B, BPFP=7.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,528B, BPFP=1.0981 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,896B, BPFP=1.0373 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,976B, BPFP=0.4727 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,976B, BPFP=0.4727 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,584B, BPFP=0.3840 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.91198166 + text_encoder-item0.clip_prompt_embeds 0.00020809 23.85200216 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.90490017 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.10963858 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00193100 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00602745 1.65859473 + vae.encoder_f1 0.00603159 1.65890348 + vae.decoder 0.00017526 0.04574519 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 2.03042314 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188256 +BPFP 0.6661 bits/point +EBPFP 1.3322 equivalent bits/point +MSE 2.030423 +---------------------- -------------------------------------------------------- +Time: 0.739s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0304 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,568B, BPFP=1.0238 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,236B, BPFP=7.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,892B, BPFP=1.0464 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 36,840B, BPFP=0.9345 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,880B, BPFP=0.4865 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,880B, BPFP=0.4865 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,444B, BPFP=0.4408 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.87114525 + text_encoder-item0.clip_prompt_embeds 0.00020908 23.88227137 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.89201298 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.10556681 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00203356 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00634616 1.70851409 + vae.encoder_f1 0.00635208 1.70843017 + vae.decoder 0.00022721 0.04786338 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 2.05433601 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 187568 +BPFP 0.6637 bits/point +EBPFP 1.3273 equivalent bits/point +MSE 2.054336 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0543 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,788B, BPFP=1.0536 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,488B, BPFP=1.0136 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 36,572B, BPFP=0.9277 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,664B, BPFP=0.2390 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,668B, BPFP=0.2391 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,688B, BPFP=0.2041 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.91370034 + text_encoder-item0.clip_prompt_embeds 0.00022947 23.86615175 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.96114407 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.09262109 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00156475 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.05448642 1.96349800 + vae.encoder_f1 0.05448771 1.96365643 + vae.decoder 0.00017748 0.02398165 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 2.16887899 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 146948 +BPFP 0.5199 bits/point +EBPFP 1.0399 equivalent bits/point +MSE 2.168879 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1689 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,156B, BPFP=0.9681 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,280B, BPFP=8.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,316B, BPFP=1.0808 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,580B, BPFP=1.0801 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,300B, BPFP=0.3403 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,300B, BPFP=0.3403 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,640B, BPFP=0.2332 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.87405578 + text_encoder-item0.clip_prompt_embeds 0.00020169 23.83793079 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.84861240 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.10008505 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00216154 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.06876971 2.56626892 + vae.encoder_f1 0.06877109 2.56613088 + vae.decoder 0.00023999 0.02224621 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 2.44774829 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 167388 +BPFP 0.5923 bits/point +EBPFP 1.1845 equivalent bits/point +MSE 2.447748 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4477 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,440B, BPFP=1.0065 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,340B, BPFP=1.0828 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,628B, BPFP=0.9798 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,184B, BPFP=0.3080 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,180B, BPFP=0.3079 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,744B, BPFP=0.5415 +⌛️ [2/4] FRONTEND: Frontend time: 0.285s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.88193234 + text_encoder-item0.clip_prompt_embeds 0.00025253 23.86439309 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.92393665 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.10183672 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00169313 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00595097 1.03043056 + vae.encoder_f1 0.00595882 1.03032660 + vae.decoder 0.00020134 0.06347562 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 1.74101200 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 169588 +BPFP 0.6000 bits/point +EBPFP 1.2001 equivalent bits/point +MSE 1.741012 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.008s, Pack+Encode: 0.285s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7410 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,692B, BPFP=1.0406 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,164B, BPFP=1.1497 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,944B, BPFP=1.0893 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,736B, BPFP=0.2859 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,736B, BPFP=0.2859 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,684B, BPFP=0.4786 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.87383485 + text_encoder-item0.clip_prompt_embeds 0.00022201 23.85844071 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.90061378 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.11280163 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00194946 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00831743 1.61488879 + vae.encoder_f1 0.00831926 1.61508226 + vae.decoder 0.00028593 0.03980881 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 2.00973218 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170028 +BPFP 0.6016 bits/point +EBPFP 1.2032 equivalent bits/point +MSE 2.009732 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0097 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,324B, BPFP=1.1261 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,260B, BPFP=7.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,764B, BPFP=1.1172 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,752B, BPFP=1.1351 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,144B, BPFP=0.4752 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,140B, BPFP=0.4752 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,792B, BPFP=0.3904 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.88860798 + text_encoder-item0.clip_prompt_embeds 0.00026808 23.88011321 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.91610994 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.10548532 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00214195 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00606586 1.72025132 + vae.encoder_f1 0.00607066 1.71974432 + vae.decoder 0.00019664 0.04333018 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 2.05913037 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 194004 +BPFP 0.6864 bits/point +EBPFP 1.3729 equivalent bits/point +MSE 2.059130 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0591 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 748B, BPFP=7.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,792B, BPFP=1.0541 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,228B, BPFP=7.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,496B, BPFP=1.1766 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,448B, BPFP=1.1274 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,444B, BPFP=0.3577 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,444B, BPFP=0.3577 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,828B, BPFP=0.3304 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.89962641 + text_encoder-item0.clip_prompt_embeds 0.00023198 23.89338981 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.96087923 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.11073491 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00198115 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.05216765 2.75722551 + vae.encoder_f1 0.05216896 2.75737190 + vae.decoder 0.00017960 0.03902811 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 2.54028140 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 176480 +BPFP 0.6244 bits/point +EBPFP 1.2489 equivalent bits/point +MSE 2.540281 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5403 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,572B, BPFP=1.0244 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,260B, BPFP=7.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,336B, BPFP=1.0825 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,780B, BPFP=1.0090 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,672B, BPFP=0.4680 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,668B, BPFP=0.4680 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,804B, BPFP=0.3602 +⌛️ [2/4] FRONTEND: Frontend time: 0.304s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.91457717 + text_encoder-item0.clip_prompt_embeds 0.00023125 23.85689132 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.93095493 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.08678142 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00190156 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00620361 1.52089489 + vae.encoder_f1 0.00620966 1.52102661 + vae.decoder 0.00020748 0.04570319 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 1.96565945 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 185912 +BPFP 0.6578 bits/point +EBPFP 1.3156 equivalent bits/point +MSE 1.965659 +---------------------- -------------------------------------------------------- +Time: 0.760s Load: 0.009s, Pack+Encode: 0.304s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9657 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,660B, BPFP=1.0363 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,236B, BPFP=7.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,848B, BPFP=1.1240 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,016B, BPFP=1.0657 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,148B, BPFP=0.3990 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,156B, BPFP=0.3991 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,284B, BPFP=0.4054 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.89319928 + text_encoder-item0.clip_prompt_embeds 0.00023066 23.87203226 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.83554859 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.10872218 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00195163 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.03159856 1.68389881 + vae.encoder_f1 0.03160188 1.68381846 + vae.decoder 0.00018417 0.04268637 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 2.04215467 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 181160 +BPFP 0.6410 bits/point +EBPFP 1.2820 equivalent bits/point +MSE 2.042155 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0422 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,964B, BPFP=1.0774 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,252B, BPFP=7.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,676B, BPFP=1.1101 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,480B, BPFP=1.1029 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,740B, BPFP=0.5148 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,740B, BPFP=0.5148 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,096B, BPFP=0.3386 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.88116511 + text_encoder-item0.clip_prompt_embeds 0.00024948 23.85984214 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.83179760 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.10512642 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00242082 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.03490865 3.13396358 + vae.encoder_f1 0.03491008 3.13422537 + vae.decoder 0.00028462 0.04917630 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 2.71506393 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 195772 +BPFP 0.6927 bits/point +EBPFP 1.3854 equivalent bits/point +MSE 2.715064 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7151 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,644B, BPFP=1.0341 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,236B, BPFP=7.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,632B, BPFP=1.1065 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,332B, BPFP=1.1245 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,496B, BPFP=0.2212 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,496B, BPFP=0.2212 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,524B, BPFP=0.5043 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.88056795 + text_encoder-item0.clip_prompt_embeds 0.00021560 23.79896341 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.83361435 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.09866114 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00222389 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00544735 0.81688213 + vae.encoder_f1 0.00544843 0.81676656 + vae.decoder 0.00018632 0.05883218 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 1.63960670 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 163188 +BPFP 0.5774 bits/point +EBPFP 1.1548 equivalent bits/point +MSE 1.639607 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6396 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 780B, BPFP=8.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,316B, BPFP=0.9897 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,252B, BPFP=7.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,588B, BPFP=1.1029 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,352B, BPFP=1.0489 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,872B, BPFP=0.4253 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,876B, BPFP=0.4254 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,588B, BPFP=0.4147 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.460s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.88550496 + text_encoder-item0.clip_prompt_embeds 0.00022698 35.88542089 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.92443686 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.09979686 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00184800 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00630479 1.38455975 + vae.encoder_f1 0.00631430 1.38457060 + vae.decoder 0.00018596 0.04187498 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 2.21711089 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183676 +BPFP 0.6499 bits/point +EBPFP 1.2998 equivalent bits/point +MSE 2.217111 +---------------------- -------------------------------------------------------- +Time: 0.760s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.460s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2171 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,404B, BPFP=1.0016 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,124B, BPFP=1.0653 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,048B, BPFP=0.9651 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,128B, BPFP=0.3987 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,124B, BPFP=0.3986 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,524B, BPFP=0.4738 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.95269640 + text_encoder-item0.clip_prompt_embeds 0.00024643 23.87413124 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.96502075 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.10328049 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00183736 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00612578 1.24713039 + vae.encoder_f1 0.00613243 1.24736261 + vae.decoder 0.00018179 0.04742898 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 1.84011300 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 178420 +BPFP 0.6313 bits/point +EBPFP 1.2626 equivalent bits/point +MSE 1.840113 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8401 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,636B, BPFP=1.1683 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,252B, BPFP=1.3192 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,316B, BPFP=1.1748 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,304B, BPFP=0.1420 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,304B, BPFP=0.1420 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,608B, BPFP=0.5374 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.88778861 + text_encoder-item0.clip_prompt_embeds 0.00024049 23.88090799 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.90815306 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.11477770 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00232222 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00526071 0.35393193 + vae.encoder_f1 0.00526072 0.35390055 + vae.decoder 0.00016981 0.05335265 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 1.42719353 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 159496 +BPFP 0.5643 bits/point +EBPFP 1.1287 equivalent bits/point +MSE 1.427194 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.4272 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,264B, BPFP=0.9827 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,848B, BPFP=1.0429 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,932B, BPFP=1.0383 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,824B, BPFP=0.4398 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,832B, BPFP=0.4399 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,888B, BPFP=0.4849 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.82486685 + text_encoder-item0.clip_prompt_embeds 0.00022843 47.90474077 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.88270426 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.09505815 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00178516 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00622977 1.39051855 + vae.encoder_f1 0.00623684 1.39042485 + vae.decoder 0.00019755 0.04695924 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 2.53454407 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 186668 +BPFP 0.6605 bits/point +EBPFP 1.3210 equivalent bits/point +MSE 2.534544 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5345 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,064B, BPFP=1.0909 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,276B, BPFP=7.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,272B, BPFP=1.0773 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,656B, BPFP=1.0312 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,188B, BPFP=0.3080 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,188B, BPFP=0.3080 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,336B, BPFP=0.3154 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.90159218 + text_encoder-item0.clip_prompt_embeds 0.00026004 23.89837409 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.96419125 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.09801211 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00189732 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00725303 1.28335309 + vae.encoder_f1 0.00725507 1.28327060 + vae.decoder 0.00017991 0.03487656 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 1.85577855 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 164792 +BPFP 0.5831 bits/point +EBPFP 1.1662 equivalent bits/point +MSE 1.855779 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.010s, Pack+Encode: 0.292s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8558 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,116B, BPFP=1.0979 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,020B, BPFP=1.1380 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,312B, BPFP=1.0225 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,396B, BPFP=0.3265 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,396B, BPFP=0.3265 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,040B, BPFP=0.2454 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.94183421 + text_encoder-item0.clip_prompt_embeds 0.00031748 23.86388367 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.97768393 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.10602438 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00187912 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.42111695 3.87594914 + vae.encoder_f1 0.42111716 3.87604356 + vae.decoder 0.00019827 0.02994329 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 3.05707693 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 165360 +BPFP 0.5851 bits/point +EBPFP 1.1702 equivalent bits/point +MSE 3.057077 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0571 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 780B, BPFP=8.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,864B, BPFP=1.0639 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,500B, BPFP=1.0958 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,424B, BPFP=1.1776 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,528B, BPFP=0.3895 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,524B, BPFP=0.3895 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,656B, BPFP=0.2947 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.85715628 + text_encoder-item0.clip_prompt_embeds 0.00024951 24.16877410 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.84738483 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.10550069 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00233483 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.10376993 4.43732929 + vae.encoder_f1 0.10377157 4.43595314 + vae.decoder 0.00019787 0.03399578 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 3.32546859 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 180568 +BPFP 0.6389 bits/point +EBPFP 1.2778 equivalent bits/point +MSE 3.325469 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3255 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,460B, BPFP=1.0092 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,704B, BPFP=1.1123 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,320B, BPFP=1.0227 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,820B, BPFP=0.3940 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,820B, BPFP=0.3940 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,280B, BPFP=0.2832 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.91667016 + text_encoder-item0.clip_prompt_embeds 0.00022350 23.87490911 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.85063381 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.10378821 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00186157 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.01346414 2.08577871 + vae.encoder_f1 0.01346933 2.08568215 + vae.decoder 0.00019243 0.03248564 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 2.22721141 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 174472 +BPFP 0.6173 bits/point +EBPFP 1.2347 equivalent bits/point +MSE 2.227211 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2272 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,932B, BPFP=1.0731 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,920B, BPFP=1.0487 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,336B, BPFP=1.1246 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,060B, BPFP=0.3366 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,060B, BPFP=0.3366 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,372B, BPFP=0.2555 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.88405530 + text_encoder-item0.clip_prompt_embeds 0.00024958 23.88683289 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.98861456 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.10383005 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00228902 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.11196710 3.26885605 + vae.encoder_f1 0.11196851 3.26980782 + vae.decoder 0.00023459 0.03529895 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 2.77689458 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 169752 +BPFP 0.6006 bits/point +EBPFP 1.2013 equivalent bits/point +MSE 2.776895 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7769 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,456B, BPFP=1.1439 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,328B, BPFP=1.1630 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,120B, BPFP=0.9669 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,240B, BPFP=0.4614 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,240B, BPFP=0.4614 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,220B, BPFP=0.3119 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.90646664 + text_encoder-item0.clip_prompt_embeds 0.00025929 23.90773387 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.92110729 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.11657324 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00173509 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00675017 1.75333071 + vae.encoder_f1 0.00675421 1.75334013 + vae.decoder 0.00023635 0.04548547 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 2.07599906 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183660 +BPFP 0.6498 bits/point +EBPFP 1.2997 equivalent bits/point +MSE 2.075999 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0760 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 788B, BPFP=8.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,236B, BPFP=1.1142 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,228B, BPFP=7.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,384B, BPFP=1.0864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,844B, BPFP=1.1121 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,852B, BPFP=0.5471 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,848B, BPFP=0.5470 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,248B, BPFP=0.3127 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.85865331 + text_encoder-item0.clip_prompt_embeds 0.00064775 23.84933247 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.90920172 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.09874471 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00223350 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00728993 2.15892458 + vae.encoder_f1 0.00729572 2.15914726 + vae.decoder 0.00026488 0.04421536 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 2.26174465 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 199480 +BPFP 0.7058 bits/point +EBPFP 1.4116 equivalent bits/point +MSE 2.261745 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2617 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 748B, BPFP=7.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,208B, BPFP=1.1104 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,200B, BPFP=1.0714 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,240B, BPFP=1.1222 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,048B, BPFP=0.4585 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,044B, BPFP=0.4584 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,752B, BPFP=0.3586 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.93139315 + text_encoder-item0.clip_prompt_embeds 0.00023188 23.88635307 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.88004761 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.10274978 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00225527 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00613207 1.48397815 + vae.encoder_f1 0.00613899 1.48466814 + vae.decoder 0.00023812 0.04623768 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 1.95022295 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 189548 +BPFP 0.6707 bits/point +EBPFP 1.3413 equivalent bits/point +MSE 1.950223 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9502 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,892B, BPFP=1.0676 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,564B, BPFP=1.1010 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,184B, BPFP=1.0954 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,364B, BPFP=0.4023 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,364B, BPFP=0.4023 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,828B, BPFP=0.3610 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.86482890 + text_encoder-item0.clip_prompt_embeds 0.00023678 23.85089032 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.93272114 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.11148876 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00190661 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00636537 1.65780783 + vae.encoder_f1 0.00636991 1.65802348 + vae.decoder 0.00025538 0.04311172 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 2.02977918 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 181252 +BPFP 0.6413 bits/point +EBPFP 1.2826 equivalent bits/point +MSE 2.029779 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0298 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,860B, BPFP=1.0633 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,932B, BPFP=1.0497 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 37,784B, BPFP=0.9584 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,920B, BPFP=0.3955 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,920B, BPFP=0.3955 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,996B, BPFP=0.2440 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.91565990 + text_encoder-item0.clip_prompt_embeds 0.00023432 23.89635755 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.78499565 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.09927547 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00186522 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.23155926 4.35991478 + vae.encoder_f1 0.23156048 4.35985947 + vae.decoder 0.00018572 0.03070690 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 3.28201382 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170480 +BPFP 0.6032 bits/point +EBPFP 1.2064 equivalent bits/point +MSE 3.282014 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2820 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,296B, BPFP=0.9870 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,312B, BPFP=1.0805 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,912B, BPFP=1.0631 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,656B, BPFP=0.4678 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,660B, BPFP=0.4678 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,980B, BPFP=0.3961 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.90932496 + text_encoder-item0.clip_prompt_embeds 0.00022528 24.11953801 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.91065197 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.09292969 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00188757 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00729824 1.83864594 + vae.encoder_f1 0.00730369 1.83877623 + vae.decoder 0.00019938 0.05298419 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 2.12098837 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188876 +BPFP 0.6683 bits/point +EBPFP 1.3366 equivalent bits/point +MSE 2.120988 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1210 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 780B, BPFP=8.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,276B, BPFP=0.9843 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,248B, BPFP=7.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,244B, BPFP=1.0750 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 35,232B, BPFP=0.8937 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,232B, BPFP=0.3087 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,236B, BPFP=0.3088 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,032B, BPFP=0.4893 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.87398664 + text_encoder-item0.clip_prompt_embeds 0.00022149 23.86110829 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.88524265 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.11019453 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00182086 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00564371 1.11781096 + vae.encoder_f1 0.00565042 1.11774397 + vae.decoder 0.00019980 0.05764095 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 1.78113997 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 164332 +BPFP 0.5815 bits/point +EBPFP 1.1629 equivalent bits/point +MSE 1.781140 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7811 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,760B, BPFP=1.0498 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,744B, BPFP=1.1156 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 36,868B, BPFP=0.9352 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,460B, BPFP=0.3275 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,464B, BPFP=0.3275 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,080B, BPFP=0.5212 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.87921850 + text_encoder-item0.clip_prompt_embeds 0.00022173 23.88006671 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.89798679 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.11218808 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00177851 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00576096 1.05173588 + vae.encoder_f1 0.00576981 1.05187464 + vae.decoder 0.00019592 0.05260247 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 1.75054583 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170456 +BPFP 0.6031 bits/point +EBPFP 1.2062 equivalent bits/point +MSE 1.750546 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.010s, Pack+Encode: 0.287s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7505 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 752B, BPFP=7.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,408B, BPFP=1.1374 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,424B, BPFP=1.1708 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,932B, BPFP=1.1143 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,848B, BPFP=0.2876 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,848B, BPFP=0.2876 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,032B, BPFP=0.3062 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.91482075 + text_encoder-item0.clip_prompt_embeds 0.00025917 23.88592186 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.92737064 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.11272237 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00215814 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00594818 1.09114218 + vae.encoder_f1 0.00595328 1.09143972 + vae.decoder 0.00023462 0.03972568 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 1.76762319 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 166540 +BPFP 0.5893 bits/point +EBPFP 1.1785 equivalent bits/point +MSE 1.767623 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7676 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,392B, BPFP=1.1353 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,820B, BPFP=1.2029 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,232B, BPFP=1.1473 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,440B, BPFP=0.2661 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,440B, BPFP=0.2661 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 6,764B, BPFP=0.2064 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.84602420 + text_encoder-item0.clip_prompt_embeds 0.00022579 23.85139551 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.88116999 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.11864525 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00244712 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.85445058 6.48711014 + vae.encoder_f1 0.85445166 6.48743582 + vae.decoder 0.00025257 0.01847547 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 4.26698976 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 162148 +BPFP 0.5737 bits/point +EBPFP 1.1474 equivalent bits/point +MSE 4.266990 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2670 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 780B, BPFP=8.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,580B, BPFP=1.0254 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,744B, BPFP=1.1156 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,940B, BPFP=1.0131 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,584B, BPFP=0.5125 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,584B, BPFP=0.5125 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,532B, BPFP=0.5045 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.91655747 + text_encoder-item0.clip_prompt_embeds 0.00025458 23.87766335 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.89384661 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.10260278 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00211212 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00628510 1.53935218 + vae.encoder_f1 0.00629234 1.53949523 + vae.decoder 0.00023521 0.05717963 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 1.97679458 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 197036 +BPFP 0.6972 bits/point +EBPFP 1.3943 equivalent bits/point +MSE 1.976795 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9768 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 752B, BPFP=7.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,876B, BPFP=1.0655 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,264B, BPFP=7.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,000B, BPFP=1.0552 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,484B, BPFP=1.1030 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,424B, BPFP=0.3422 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,420B, BPFP=0.3421 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,384B, BPFP=0.4084 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.95473282 + text_encoder-item0.clip_prompt_embeds 0.00022807 24.14777166 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.98089190 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.10432056 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00223398 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00573429 1.11058462 + vae.encoder_f1 0.00574192 1.11057401 + vae.decoder 0.00017875 0.04543654 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 1.78376749 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 174656 +BPFP 0.6180 bits/point +EBPFP 1.2360 equivalent bits/point +MSE 1.783767 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7838 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,464B, BPFP=1.2803 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,224B, BPFP=7.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,452B, BPFP=1.3354 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,640B, BPFP=1.3606 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,796B, BPFP=0.4547 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,800B, BPFP=0.4547 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,548B, BPFP=0.4135 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.89318911 + text_encoder-item0.clip_prompt_embeds 0.00027120 23.80861912 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.90032825 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.12610735 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00314279 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00781570 1.92024720 + vae.encoder_f1 0.00781878 1.92047572 + vae.decoder 0.00029724 0.05420362 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 2.15247458 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 204740 +BPFP 0.7244 bits/point +EBPFP 1.4489 equivalent bits/point +MSE 2.152475 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1525 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 780B, BPFP=8.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,628B, BPFP=1.0319 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,228B, BPFP=7.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,912B, BPFP=1.1292 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,960B, BPFP=1.0897 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,696B, BPFP=0.4073 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,700B, BPFP=0.4074 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,340B, BPFP=0.5902 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.91897845 + text_encoder-item0.clip_prompt_embeds 0.00022930 23.82759444 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.81119833 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.10616938 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00199051 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00577752 1.09524632 + vae.encoder_f1 0.00578475 1.09511757 + vae.decoder 0.00024190 0.05561842 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 1.76937140 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 189296 +BPFP 0.6698 bits/point +EBPFP 1.3396 equivalent bits/point +MSE 1.769371 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7694 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 748B, BPFP=7.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,612B, BPFP=1.1650 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,388B, BPFP=1.1679 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,396B, BPFP=1.1515 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,280B, BPFP=0.4620 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,276B, BPFP=0.4620 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,212B, BPFP=0.2201 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.97644432 + text_encoder-item0.clip_prompt_embeds 0.00028764 23.86242940 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.95269566 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.10830398 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00257234 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.03343784 3.12747526 + vae.encoder_f1 0.03344063 3.12808609 + vae.decoder 0.00016139 0.02427479 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 2.70957678 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188232 +BPFP 0.6660 bits/point +EBPFP 1.3320 equivalent bits/point +MSE 2.709577 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7096 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 788B, BPFP=8.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,068B, BPFP=0.9562 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,064B, BPFP=1.0604 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,180B, BPFP=1.0699 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,684B, BPFP=0.4987 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,684B, BPFP=0.4987 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,696B, BPFP=0.4485 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.88441475 + text_encoder-item0.clip_prompt_embeds 0.00023094 48.67952263 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.85528736 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.09734160 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00175429 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00637455 1.64483535 + vae.encoder_f1 0.00637988 1.64493585 + vae.decoder 0.00020059 0.05153318 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 2.67342771 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 194472 +BPFP 0.6881 bits/point +EBPFP 1.3762 equivalent bits/point +MSE 2.673428 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6734 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 756B, BPFP=7.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,752B, BPFP=1.0487 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,264B, BPFP=7.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,864B, BPFP=1.1253 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,548B, BPFP=1.1300 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,428B, BPFP=0.3880 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,428B, BPFP=0.3880 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,800B, BPFP=0.4517 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.95175592 + text_encoder-item0.clip_prompt_embeds 0.00025217 23.88924048 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.98244276 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.09576698 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00211535 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00581597 1.14038134 + vae.encoder_f1 0.00582356 1.14045620 + vae.decoder 0.00019494 0.05027879 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 1.79101609 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183892 +BPFP 0.6507 bits/point +EBPFP 1.3013 equivalent bits/point +MSE 1.791016 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7910 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,540B, BPFP=1.0200 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,272B, BPFP=7.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,304B, BPFP=0.9987 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 37,876B, BPFP=0.9607 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,664B, BPFP=0.2390 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,668B, BPFP=0.2391 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,408B, BPFP=0.2871 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.94239442 + text_encoder-item0.clip_prompt_embeds 0.00026975 23.85927988 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.92176914 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.09362016 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00213500 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 1.11695218 4.89502192 + vae.encoder_f1 1.11695278 4.89502239 + vae.decoder 0.00019720 0.04241312 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 3.53045749 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 150548 +BPFP 0.5327 bits/point +EBPFP 1.0654 equivalent bits/point +MSE 3.530457 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5305 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,284B, BPFP=1.1207 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,172B, BPFP=1.0692 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,196B, BPFP=1.1210 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,948B, BPFP=0.4112 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,948B, BPFP=0.4112 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,320B, BPFP=0.4065 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.90833767 + text_encoder-item0.clip_prompt_embeds 0.00025545 23.87542275 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.79091682 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.09561909 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00202726 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.01535016 2.35649729 + vae.encoder_f1 0.01535382 2.35617876 + vae.decoder 0.00021460 0.04607920 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 2.35393046 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 184924 +BPFP 0.6543 bits/point +EBPFP 1.3086 equivalent bits/point +MSE 2.353930 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3539 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,052B, BPFP=1.0893 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,616B, BPFP=1.1864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,440B, BPFP=1.1272 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,884B, BPFP=0.3187 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,884B, BPFP=0.3187 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,024B, BPFP=0.5806 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.92695498 + text_encoder-item0.clip_prompt_embeds 0.00022628 23.88978795 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.91739235 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.10833402 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00233664 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00589589 1.04802811 + vae.encoder_f1 0.00590398 1.04792953 + vae.decoder 0.00017838 0.06419732 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 1.75030689 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 179956 +BPFP 0.6367 bits/point +EBPFP 1.2735 equivalent bits/point +MSE 1.750307 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7503 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,748B, BPFP=1.0482 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,468B, BPFP=1.0932 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,124B, BPFP=1.0178 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,116B, BPFP=0.5053 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,116B, BPFP=0.5053 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,792B, BPFP=0.2683 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.88283722 + text_encoder-item0.clip_prompt_embeds 0.00031548 23.85921858 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.89735203 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.09922702 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00163379 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00725484 2.15568018 + vae.encoder_f1 0.00725992 2.15576696 + vae.decoder 0.00019960 0.03643490 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 2.25950386 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188436 +BPFP 0.6667 bits/point +EBPFP 1.3335 equivalent bits/point +MSE 2.259504 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2595 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 756B, BPFP=7.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,376B, BPFP=0.9978 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,276B, BPFP=7.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,376B, BPFP=1.0045 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 32,288B, BPFP=0.8190 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,072B, BPFP=0.4283 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,076B, BPFP=0.4284 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,076B, BPFP=0.3075 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.88530842 + text_encoder-item0.clip_prompt_embeds 0.00021831 36.44577753 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 1.06162729 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.09231385 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00149692 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00923516 2.02274227 + vae.encoder_f1 0.00923823 2.02273846 + vae.decoder 0.00019521 0.03090475 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 2.52616278 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170348 +BPFP 0.6027 bits/point +EBPFP 1.2055 equivalent bits/point +MSE 2.526163 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5262 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 748B, BPFP=7.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,428B, BPFP=1.0049 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,332B, BPFP=1.0821 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,424B, BPFP=1.1268 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,196B, BPFP=0.4455 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,196B, BPFP=0.4455 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,380B, BPFP=0.3473 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.90973171 + text_encoder-item0.clip_prompt_embeds 0.00062166 23.90547002 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.86703281 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.10331273 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00210074 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00831779 2.02175951 + vae.encoder_f1 0.00832197 2.02091050 + vae.decoder 0.00023271 0.04047240 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 2.19909175 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 187000 +BPFP 0.6617 bits/point +EBPFP 1.3233 equivalent bits/point +MSE 2.199092 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1991 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,960B, BPFP=1.0768 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,216B, BPFP=1.0727 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,644B, BPFP=1.0563 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,628B, BPFP=0.3605 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,632B, BPFP=0.3606 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,528B, BPFP=0.2908 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.92404826 + text_encoder-item0.clip_prompt_embeds 0.00022938 24.69210802 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.86400223 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.09456245 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00213304 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00626977 1.39711046 + vae.encoder_f1 0.00627489 1.39699507 + vae.decoder 0.00017842 0.04443347 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 1.93022945 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 171660 +BPFP 0.6074 bits/point +EBPFP 1.2148 equivalent bits/point +MSE 1.930229 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9302 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,488B, BPFP=1.0130 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,204B, BPFP=1.0718 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,032B, BPFP=1.0915 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,160B, BPFP=0.3839 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,160B, BPFP=0.3839 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,564B, BPFP=0.5360 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.84534192 + text_encoder-item0.clip_prompt_embeds 0.00022180 23.85714497 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.86770830 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.10486587 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00187802 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00585720 1.16868544 + vae.encoder_f1 0.00586586 1.16839671 + vae.decoder 0.00016520 0.05727239 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 1.80429215 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183692 +BPFP 0.6500 bits/point +EBPFP 1.2999 equivalent bits/point +MSE 1.804292 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8043 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 756B, BPFP=7.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,236B, BPFP=0.9789 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,228B, BPFP=7.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,996B, BPFP=1.0549 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,028B, BPFP=1.0914 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,756B, BPFP=0.3015 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,756B, BPFP=0.3015 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,032B, BPFP=0.2756 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.441s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.91587575 + text_encoder-item0.clip_prompt_embeds 0.00025784 23.88886211 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.74035888 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.10483146 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00209922 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00734802 1.66879261 + vae.encoder_f1 0.00734987 1.66876292 + vae.decoder 0.00018093 0.02823452 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 2.03373005 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 163840 +BPFP 0.5797 bits/point +EBPFP 1.1594 equivalent bits/point +MSE 2.033730 +---------------------- -------------------------------------------------------- +Time: 0.738s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.441s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0337 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,376B, BPFP=1.1331 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,680B, BPFP=1.1104 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,988B, BPFP=1.0650 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,404B, BPFP=0.4792 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,408B, BPFP=0.4792 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,520B, BPFP=0.3821 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.91595586 + text_encoder-item0.clip_prompt_embeds 0.00023510 23.88534268 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.84326935 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.10018366 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00193425 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00637359 1.73882318 + vae.encoder_f1 0.00637830 1.73874724 + vae.decoder 0.00018566 0.04973733 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 2.06843097 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 191436 +BPFP 0.6774 bits/point +EBPFP 1.3547 equivalent bits/point +MSE 2.068431 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0684 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 752B, BPFP=7.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,440B, BPFP=1.0065 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,248B, BPFP=7.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,680B, BPFP=1.1104 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,076B, BPFP=1.1180 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,732B, BPFP=0.3621 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,728B, BPFP=0.3621 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,844B, BPFP=0.2394 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.90247560 + text_encoder-item0.clip_prompt_embeds 0.00026418 24.13310843 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.86125221 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.10936234 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00210789 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.01530954 2.17075729 + vae.encoder_f1 0.01531230 2.17064929 + vae.decoder 0.00017892 0.03087719 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 2.27346434 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 172552 +BPFP 0.6105 bits/point +EBPFP 1.2211 equivalent bits/point +MSE 2.273464 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2735 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,900B, BPFP=1.0687 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,232B, BPFP=7.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,800B, BPFP=1.1201 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,604B, BPFP=1.1314 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,324B, BPFP=0.4627 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,316B, BPFP=0.4626 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,152B, BPFP=0.4014 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.83537610 + text_encoder-item0.clip_prompt_embeds 0.00021481 23.88300485 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.80878210 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.10422529 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00218356 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00591154 1.56577611 + vae.encoder_f1 0.00591973 1.56602144 + vae.decoder 0.00025286 0.04940570 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 1.98831622 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 192156 +BPFP 0.6799 bits/point +EBPFP 1.3598 equivalent bits/point +MSE 1.988316 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9883 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,260B, BPFP=1.1174 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,232B, BPFP=7.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,096B, BPFP=1.1442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,356B, BPFP=1.1505 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,880B, BPFP=0.2881 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,880B, BPFP=0.2881 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,540B, BPFP=0.4742 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.91315047 + text_encoder-item0.clip_prompt_embeds 0.00023458 23.88731483 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.89787245 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.12000291 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00217694 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00588703 0.91584790 + vae.encoder_f1 0.00589573 0.91548496 + vae.decoder 0.00053402 0.05236999 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 1.68797930 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 173068 +BPFP 0.6124 bits/point +EBPFP 1.2247 equivalent bits/point +MSE 1.687979 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6880 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,864B, BPFP=1.0639 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,248B, BPFP=7.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,796B, BPFP=1.1198 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,640B, BPFP=1.0308 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,360B, BPFP=0.4175 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,360B, BPFP=0.4175 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,140B, BPFP=0.2789 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.87560240 + text_encoder-item0.clip_prompt_embeds 0.00022882 36.17288538 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.91523418 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.10459452 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00205274 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00659691 1.83168447 + vae.encoder_f1 0.00660300 1.83162057 + vae.decoder 0.00023739 0.03603844 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 2.43152677 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 178236 +BPFP 0.6306 bits/point +EBPFP 1.2613 equivalent bits/point +MSE 2.431527 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4315 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 780B, BPFP=8.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,340B, BPFP=0.9930 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,236B, BPFP=7.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,600B, BPFP=1.1039 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,296B, BPFP=1.0475 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,616B, BPFP=0.2841 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,612B, BPFP=0.2840 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,000B, BPFP=0.5493 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.89967076 + text_encoder-item0.clip_prompt_embeds 0.00023928 23.88853025 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.91875811 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.10329778 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00190216 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00583864 0.98019779 + vae.encoder_f1 0.00583800 0.98021114 + vae.decoder 0.00018889 0.05766743 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 1.71779668 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 169532 +BPFP 0.5998 bits/point +EBPFP 1.1997 equivalent bits/point +MSE 1.717797 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7178 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,164B, BPFP=1.1044 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,228B, BPFP=7.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,140B, BPFP=1.1477 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,140B, BPFP=1.1450 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 16,548B, BPFP=0.2525 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 16,552B, BPFP=0.2526 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,224B, BPFP=0.4036 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.91650287 + text_encoder-item0.clip_prompt_embeds 0.00024821 23.87855325 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.71124558 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.10314617 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00244315 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00570467 0.78987992 + vae.encoder_f1 0.00570488 0.78980911 + vae.decoder 0.00017302 0.04753446 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 1.62803512 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 165808 +BPFP 0.5867 bits/point +EBPFP 1.1733 equivalent bits/point +MSE 1.628035 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6280 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 756B, BPFP=7.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,880B, BPFP=1.0660 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,236B, BPFP=7.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,820B, BPFP=1.1218 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,636B, BPFP=1.0815 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,300B, BPFP=0.2640 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,300B, BPFP=0.2640 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,712B, BPFP=0.2354 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.90885592 + text_encoder-item0.clip_prompt_embeds 0.00021458 24.16494394 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.78637466 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.10714391 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00197467 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00914783 1.76483047 + vae.encoder_f1 0.00914958 1.76531422 + vae.decoder 0.00017527 0.02979274 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 2.08589705 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 158692 +BPFP 0.5615 bits/point +EBPFP 1.1230 equivalent bits/point +MSE 2.085897 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0859 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 784B, BPFP=8.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,744B, BPFP=1.1829 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,240B, BPFP=7.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,560B, BPFP=1.1818 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,812B, BPFP=1.1113 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 19,108B, BPFP=0.2916 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 19,108B, BPFP=0.2916 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,364B, BPFP=0.5909 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.90975587 + text_encoder-item0.clip_prompt_embeds 0.00022150 23.89884546 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.91946611 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.11152548 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00228109 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00578482 0.96350825 + vae.encoder_f1 0.00579739 0.96339792 + vae.decoder 0.00017668 0.05776810 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 1.71072473 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 176772 +BPFP 0.6255 bits/point +EBPFP 1.2509 equivalent bits/point +MSE 1.710725 +---------------------- -------------------------------------------------------- +Time: 0.739s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7107 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,520B, BPFP=1.0173 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,236B, BPFP=7.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,108B, BPFP=1.0640 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,660B, BPFP=1.0567 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,344B, BPFP=0.3257 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,344B, BPFP=0.3257 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,824B, BPFP=0.3914 +⌛️ [2/4] FRONTEND: Frontend time: 0.286s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.442s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.90577531 + text_encoder-item0.clip_prompt_embeds 0.00023894 23.87876251 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.89781713 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.09882965 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00186837 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00958025 1.68237722 + vae.encoder_f1 0.00958229 1.68213487 + vae.decoder 0.00019995 0.04615161 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 2.04158591 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 169856 +BPFP 0.6010 bits/point +EBPFP 1.2020 equivalent bits/point +MSE 2.041586 +---------------------- -------------------------------------------------------- +Time: 0.736s Load: 0.008s, Pack+Encode: 0.286s, Decode+Unpack: 0.442s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0416 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,240B, BPFP=1.1147 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,280B, BPFP=8.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,224B, BPFP=1.2357 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,736B, BPFP=1.1347 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,576B, BPFP=0.2224 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,576B, BPFP=0.2224 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,796B, BPFP=0.5736 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.87470961 + text_encoder-item0.clip_prompt_embeds 0.00023387 23.90160182 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.89225483 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.12165363 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00229624 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00567713 0.88091487 + vae.encoder_f1 0.00567905 0.88093281 + vae.decoder 0.00019376 0.06577697 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 1.67386726 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 168252 +BPFP 0.5953 bits/point +EBPFP 1.1906 equivalent bits/point +MSE 1.673867 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6739 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 780B, BPFP=8.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,860B, BPFP=1.0633 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,264B, BPFP=7.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,784B, BPFP=1.1188 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,108B, BPFP=1.1188 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,580B, BPFP=0.3293 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,580B, BPFP=0.3293 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,988B, BPFP=0.3353 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.87024061 + text_encoder-item0.clip_prompt_embeds 0.00024281 23.87430457 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.85196362 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.10369578 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00209730 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.02387581 1.75176573 + vae.encoder_f1 0.02387858 1.75193846 + vae.decoder 0.00018648 0.03701628 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 2.07289260 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 171996 +BPFP 0.6086 bits/point +EBPFP 1.2171 equivalent bits/point +MSE 2.072893 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0729 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,848B, BPFP=1.0617 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,232B, BPFP=7.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,512B, BPFP=1.0968 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,400B, BPFP=1.0755 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,088B, BPFP=0.5049 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,092B, BPFP=0.5049 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,288B, BPFP=0.2529 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.92189455 + text_encoder-item0.clip_prompt_embeds 0.00022399 23.89382736 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.83574667 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.10437427 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00212835 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.01169517 2.54846334 + vae.encoder_f1 0.01169969 2.54861116 + vae.decoder 0.00021186 0.02911269 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 2.44200631 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 190280 +BPFP 0.6733 bits/point +EBPFP 1.3465 equivalent bits/point +MSE 2.442006 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4420 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,584B, BPFP=1.0260 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,232B, BPFP=7.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,080B, BPFP=1.0617 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,116B, BPFP=1.1697 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,088B, BPFP=0.3828 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,084B, BPFP=0.3828 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,480B, BPFP=0.4419 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.87193688 + text_encoder-item0.clip_prompt_embeds 0.00022123 23.88086149 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.85610580 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.10182590 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00232236 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.32749966 6.11334515 + vae.encoder_f1 0.32750070 6.11270523 + vae.decoder 0.00039956 0.04671349 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 4.09671425 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183480 +BPFP 0.6492 bits/point +EBPFP 1.2984 equivalent bits/point +MSE 4.096714 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0967 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 756B, BPFP=7.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,848B, BPFP=1.0617 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,224B, BPFP=7.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,700B, BPFP=1.1120 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,152B, BPFP=1.0438 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,372B, BPFP=0.3109 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,372B, BPFP=0.3109 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,084B, BPFP=0.4298 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.85911854 + text_encoder-item0.clip_prompt_embeds 0.00024675 23.90081761 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.82870045 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.10066352 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00198687 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00566967 1.05377734 + vae.encoder_f1 0.00567867 1.05381632 + vae.decoder 0.00017839 0.04724488 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 1.75087166 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 169560 +BPFP 0.5999 bits/point +EBPFP 1.1999 equivalent bits/point +MSE 1.750872 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7509 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 792B, BPFP=8.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,456B, BPFP=1.0087 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,252B, BPFP=7.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,660B, BPFP=1.0276 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,244B, BPFP=1.0715 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,012B, BPFP=0.2596 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,016B, BPFP=0.2596 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,044B, BPFP=0.6727 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.84664583 + text_encoder-item0.clip_prompt_embeds 0.00022364 48.20488366 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.97656803 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.09026910 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00185652 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00580750 0.98126197 + vae.encoder_f1 0.00580664 0.98128641 + vae.decoder 0.00018044 0.06278535 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 2.35431816 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 170528 +BPFP 0.6034 bits/point +EBPFP 1.2067 equivalent bits/point +MSE 2.354318 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3543 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,328B, BPFP=0.9913 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,248B, BPFP=7.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,544B, BPFP=1.0994 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,404B, BPFP=0.9995 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,572B, BPFP=0.3597 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,572B, BPFP=0.3597 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,240B, BPFP=0.2820 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.86574952 + text_encoder-item0.clip_prompt_embeds 0.00030118 24.13272161 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.79989548 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.10272595 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00199952 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.03869025 2.50461388 + vae.encoder_f1 0.03869358 2.50432181 + vae.decoder 0.00021614 0.03864111 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 2.42879213 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 168732 +BPFP 0.5970 bits/point +EBPFP 1.1940 equivalent bits/point +MSE 2.428792 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4288 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 764B, BPFP=7.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,316B, BPFP=0.9897 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,256B, BPFP=7.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,064B, BPFP=1.0604 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,908B, BPFP=1.0376 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,336B, BPFP=0.4934 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,336B, BPFP=0.4934 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,408B, BPFP=0.2566 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.90539320 + text_encoder-item0.clip_prompt_embeds 0.00023260 23.86755107 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.94733028 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.09786453 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00187175 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00839879 2.59285665 + vae.encoder_f1 0.00840224 2.59261775 + vae.decoder 0.00019463 0.03691503 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 2.46246020 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 186440 +BPFP 0.6597 bits/point +EBPFP 1.3194 equivalent bits/point +MSE 2.462460 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.446s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4625 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 776B, BPFP=8.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,620B, BPFP=1.1661 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,252B, BPFP=7.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,712B, BPFP=1.1942 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,420B, BPFP=1.0506 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,280B, BPFP=0.4468 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,284B, BPFP=0.4468 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,256B, BPFP=0.2825 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.86755848 + text_encoder-item0.clip_prompt_embeds 0.00023544 23.88764247 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.91152716 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.11019967 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00245232 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.01160815 2.25835180 + vae.encoder_f1 0.01161249 2.25885987 + vae.decoder 0.00021720 0.03475772 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 2.30836167 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 184652 +BPFP 0.6533 bits/point +EBPFP 1.3067 equivalent bits/point +MSE 2.308362 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3084 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 772B, BPFP=8.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,548B, BPFP=1.0211 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,260B, BPFP=7.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,492B, BPFP=1.0951 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,200B, BPFP=1.0704 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,928B, BPFP=0.2888 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,928B, BPFP=0.2888 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 8,716B, BPFP=0.2660 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.90068110 + text_encoder-item0.clip_prompt_embeds 0.00022923 23.82976317 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.87678814 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.10315027 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00202346 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.02989292 2.26098061 + vae.encoder_f1 0.02989391 2.26165724 + vae.decoder 0.00034944 0.03334019 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 2.30756620 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 161896 +BPFP 0.5728 bits/point +EBPFP 1.1457 equivalent bits/point +MSE 2.307566 +---------------------- -------------------------------------------------------- +Time: 0.743s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3076 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 768B, BPFP=8.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,148B, BPFP=1.1023 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,244B, BPFP=7.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,256B, BPFP=1.0760 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,368B, BPFP=1.1254 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 812B, BPFP=8.4583 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,656B, BPFP=0.9004 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 1,332B, BPFP=8.3250 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,840B, BPFP=0.9610 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,412B, BPFP=0.7460 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,180B, BPFP=0.4453 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,180B, BPFP=0.4453 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,780B, BPFP=0.3595 +⌛️ [2/4] FRONTEND: Frontend time: 0.285s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.92272178 + text_encoder-item0.clip_prompt_embeds 0.00024627 23.88754101 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.93769751 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.10289308 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00188607 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.98351804 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.78055668 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.64237404 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.07430697 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00152254 + vae.encoder_f0 0.00613025 1.32215035 + vae.encoder_f1 0.00613536 1.32194710 + vae.decoder 0.00018697 0.04647390 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 1.87500814 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 187976 +BPFP 0.6651 bits/point +EBPFP 1.3302 equivalent bits/point +MSE 1.875008 +---------------------- -------------------------------------------------------- +Time: 0.737s Load: 0.009s, Pack+Encode: 0.285s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8750 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.007/hyperprior-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.6255 bits/point +Avg EBPFP 1.2509 equivalent bits/point +Avg MSE 2.228153 +Avg Time 0.751s +------------------------ ---------------------------- diff --git a/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log b/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..31f1e9c360f506176a48eb605b43041a0b21096a --- /dev/null +++ b/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log @@ -0,0 +1,21862 @@ +Experiment: dtufc_elic-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: sd35 + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 506 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,408B, BPFP=1.4080 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,712B, BPFP=10.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,784B, BPFP=1.3623 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,760B, BPFP=1.4397 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,384B, BPFP=0.6010 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,380B, BPFP=0.6009 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,168B, BPFP=0.6155 +⌛️ [2/4] FRONTEND: Frontend time: 3.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.656s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.48561128 + text_encoder-item0.clip_prompt_embeds 0.00025464 34.77013367 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.46949377 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.08880132 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00251825 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00635250 1.03789592 + vae.encoder_f1 0.00635834 1.03853095 + vae.decoder 0.00019940 0.03084335 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 3.63147591 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 246308 +BPFP 0.8715 bits/point +EBPFP 1.7430 equivalent bits/point +MSE 3.631476 +---------------------- -------------------------------------------------------- +Time: 4.802s Load: 0.007s, Pack+Encode: 3.139s, Decode+Unpack: 1.656s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6315 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,100B, BPFP=1.3663 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,116B, BPFP=1.3893 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,436B, BPFP=1.3554 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,592B, BPFP=0.4210 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,588B, BPFP=0.4210 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,128B, BPFP=0.4922 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.45190084 + text_encoder-item0.clip_prompt_embeds 0.00022609 34.75806827 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.48146625 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.12356106 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00343898 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01130640 1.14544678 + vae.encoder_f1 0.01130902 1.14675188 + vae.decoder 0.00020860 0.02982000 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 3.68271472 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215400 +BPFP 0.7621 bits/point +EBPFP 1.5243 equivalent bits/point +MSE 3.682715 +---------------------- -------------------------------------------------------- +Time: 3.768s Load: 0.008s, Pack+Encode: 2.153s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6827 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,256B, BPFP=13.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,428B, BPFP=1.1402 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,956B, BPFP=12.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,212B, BPFP=1.2347 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,916B, BPFP=1.2408 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,896B, BPFP=0.2273 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,896B, BPFP=0.2273 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,664B, BPFP=0.3560 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.44006622 + text_encoder-item0.clip_prompt_embeds 0.00022402 360.09486607 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 0.55094485 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.08537365 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00320146 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 1.19630027 3.70893049 + vae.encoder_f1 1.19630098 3.71125603 + vae.decoder 0.00023596 0.02635288 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 13.37889749 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 177740 +BPFP 0.6289 bits/point +EBPFP 1.2578 equivalent bits/point +MSE 13.378897 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.007s, Pack+Encode: 2.152s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 13.3789 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,224B, BPFP=12.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,768B, BPFP=1.3214 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,764B, BPFP=11.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,308B, BPFP=1.4049 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,436B, BPFP=1.3301 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,348B, BPFP=0.5241 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,344B, BPFP=0.5240 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,740B, BPFP=0.7855 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.608s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.45499655 + text_encoder-item0.clip_prompt_embeds 0.00030342 23.84586800 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.46467714 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.10296512 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00273535 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00586287 0.83930212 + vae.encoder_f1 0.00587438 0.83824712 + vae.decoder 0.00017677 0.04747233 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 3.25582214 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 237448 +BPFP 0.8402 bits/point +EBPFP 1.6803 equivalent bits/point +MSE 3.255822 +---------------------- -------------------------------------------------------- +Time: 3.770s Load: 0.008s, Pack+Encode: 2.153s, Decode+Unpack: 1.608s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2558 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,296B, BPFP=1.1223 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,856B, BPFP=1.2058 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,096B, BPFP=1.2707 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,868B, BPFP=0.3795 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,860B, BPFP=0.3793 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,896B, BPFP=0.4241 +⌛️ [2/4] FRONTEND: Frontend time: 2.160s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.43047563 + text_encoder-item0.clip_prompt_embeds 0.00024120 34.76268263 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.49915867 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.11430756 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00285828 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00779453 1.01944864 + vae.encoder_f1 0.00779802 1.01792669 + vae.decoder 0.00023829 0.02969345 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 3.62324963 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 200380 +BPFP 0.7090 bits/point +EBPFP 1.4180 equivalent bits/point +MSE 3.623250 +---------------------- -------------------------------------------------------- +Time: 3.769s Load: 0.008s, Pack+Encode: 2.160s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6232 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,360B, BPFP=1.2662 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,828B, BPFP=11.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,380B, BPFP=1.3295 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,780B, BPFP=1.2627 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,828B, BPFP=0.5772 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,816B, BPFP=0.5770 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,360B, BPFP=0.5298 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.46654900 + text_encoder-item0.clip_prompt_embeds 0.00025651 34.70652394 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.46773295 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.10921366 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00317718 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00655775 1.07150722 + vae.encoder_f1 0.00656268 1.07048213 + vae.decoder 0.00020283 0.02923671 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 3.64580307 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 232036 +BPFP 0.8210 bits/point +EBPFP 1.6420 equivalent bits/point +MSE 3.645803 +---------------------- -------------------------------------------------------- +Time: 3.771s Load: 0.009s, Pack+Encode: 2.161s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6458 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,216B, BPFP=12.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,812B, BPFP=1.0568 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,820B, BPFP=11.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,192B, BPFP=1.1519 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,248B, BPFP=1.1477 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,120B, BPFP=0.5664 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,124B, BPFP=0.5665 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,204B, BPFP=0.6776 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.47031935 + text_encoder-item0.clip_prompt_embeds 0.00022242 34.68023708 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.50093136 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.08834570 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00269741 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00593415 0.90964299 + vae.encoder_f1 0.00594307 0.90872890 + vae.decoder 0.00018992 0.04021293 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 3.57038990 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227252 +BPFP 0.8041 bits/point +EBPFP 1.6082 equivalent bits/point +MSE 3.570390 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.007s, Pack+Encode: 2.149s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5704 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,176B, BPFP=12.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,612B, BPFP=1.3003 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,804B, BPFP=11.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,468B, BPFP=1.4179 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,452B, BPFP=1.3305 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,400B, BPFP=0.4944 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,408B, BPFP=0.4945 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,348B, BPFP=0.4989 +⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.45715479 + text_encoder-item0.clip_prompt_embeds 0.00022110 35.90558628 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.48182459 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.10724829 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00330016 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00641770 0.91627687 + vae.encoder_f1 0.00642053 0.91336274 + vae.decoder 0.00017498 0.02475019 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 3.60415158 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224184 +BPFP 0.7932 bits/point +EBPFP 1.5864 equivalent bits/point +MSE 3.604152 +---------------------- -------------------------------------------------------- +Time: 3.769s Load: 0.008s, Pack+Encode: 2.158s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6042 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,748B, BPFP=1.0482 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,844B, BPFP=11.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,828B, BPFP=1.2036 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,184B, BPFP=1.3237 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,868B, BPFP=0.4252 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,880B, BPFP=0.4254 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,720B, BPFP=0.6323 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.43914723 + text_encoder-item0.clip_prompt_embeds 0.00021654 34.77260256 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.47976217 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.13476141 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00288607 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00577698 0.81280011 + vae.encoder_f1 0.00578348 0.81290418 + vae.decoder 0.00017559 0.03828102 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 3.52993225 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 214788 +BPFP 0.7600 bits/point +EBPFP 1.5200 equivalent bits/point +MSE 3.529932 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.007s, Pack+Encode: 2.147s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5299 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,108B, BPFP=11.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,748B, BPFP=1.1834 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,712B, BPFP=10.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,080B, BPFP=1.3052 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,864B, BPFP=1.3663 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,508B, BPFP=0.4960 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,524B, BPFP=0.4963 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,420B, BPFP=0.5011 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.53595881 + text_encoder-item0.clip_prompt_embeds 0.00022160 35.03558112 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.45290751 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.18724619 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00258608 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00668450 0.88632262 + vae.encoder_f1 0.00668875 0.88568670 + vae.decoder 0.00023059 0.03116024 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 3.57217436 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 223480 +BPFP 0.7907 bits/point +EBPFP 1.5815 equivalent bits/point +MSE 3.572174 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5722 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,188B, BPFP=12.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,200B, BPFP=1.3799 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,732B, BPFP=10.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,792B, BPFP=1.3630 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,456B, BPFP=1.3813 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,880B, BPFP=0.4254 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,888B, BPFP=0.4255 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,448B, BPFP=0.3188 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.47899151 + text_encoder-item0.clip_prompt_embeds 0.00023190 105.03321158 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.41476469 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.10631112 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00259309 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.04018118 1.35694838 + vae.encoder_f1 0.04018488 1.35244203 + vae.decoder 0.00016201 0.02029706 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 5.61549096 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 211100 +BPFP 0.7469 bits/point +EBPFP 1.4939 equivalent bits/point +MSE 5.615491 +---------------------- -------------------------------------------------------- +Time: 3.764s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6155 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,152B, BPFP=12.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,500B, BPFP=1.1499 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,736B, BPFP=10.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,720B, BPFP=1.1948 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,864B, BPFP=1.3155 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,548B, BPFP=0.4966 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,556B, BPFP=0.4968 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,296B, BPFP=0.5278 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.51148844 + text_encoder-item0.clip_prompt_embeds 0.00023140 45.75787169 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.48767986 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.11076204 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00267181 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.04874706 1.46654081 + vae.encoder_f1 0.04875064 1.47603106 + vae.decoder 0.00019641 0.02380780 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 4.11988631 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 220888 +BPFP 0.7816 bits/point +EBPFP 1.5631 equivalent bits/point +MSE 4.119886 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1199 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,220B, BPFP=12.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,092B, BPFP=1.2300 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,780B, BPFP=11.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,292B, BPFP=1.2412 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,300B, BPFP=1.2251 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,720B, BPFP=0.5908 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,720B, BPFP=0.5908 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,664B, BPFP=0.3254 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.45355173 + text_encoder-item0.clip_prompt_embeds 0.00030893 34.75774275 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.46145267 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.14164274 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00343728 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01360236 1.38856089 + vae.encoder_f1 0.01360807 1.38590598 + vae.decoder 0.00023006 0.02627112 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 3.79490225 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224304 +BPFP 0.7936 bits/point +EBPFP 1.5873 equivalent bits/point +MSE 3.794902 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7949 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,156B, BPFP=12.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,908B, BPFP=1.3404 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,476B, BPFP=1.3373 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,948B, BPFP=1.3684 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,344B, BPFP=0.2189 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,340B, BPFP=0.2188 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,736B, BPFP=0.1750 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.47424376 + text_encoder-item0.clip_prompt_embeds 0.00024198 166.95594900 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.46597896 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.09524406 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00282476 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 1.67190456 3.89380527 + vae.encoder_f1 1.67190480 3.89380431 + vae.decoder 0.00017417 0.01227962 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 8.41127920 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 178220 +BPFP 0.6306 bits/point +EBPFP 1.2612 equivalent bits/point +MSE 8.411279 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.007s, Pack+Encode: 2.149s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.4113 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,212B, BPFP=12.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,040B, BPFP=1.2229 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,772B, BPFP=11.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,948B, BPFP=1.2945 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,256B, BPFP=1.4269 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,384B, BPFP=0.6010 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,368B, BPFP=0.6007 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,780B, BPFP=0.6036 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.43624159 + text_encoder-item0.clip_prompt_embeds 0.00025129 34.77436545 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.48833709 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.14161825 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00291780 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00621760 1.02001333 + vae.encoder_f1 0.00622505 1.01812887 + vae.decoder 0.00025114 0.03496560 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 3.62553893 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 243276 +BPFP 0.8608 bits/point +EBPFP 1.7216 equivalent bits/point +MSE 3.625539 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6255 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,164B, BPFP=12.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,052B, BPFP=1.0893 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,800B, BPFP=11.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,248B, BPFP=1.2377 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,276B, BPFP=1.2245 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,156B, BPFP=0.6432 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,160B, BPFP=0.6433 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,252B, BPFP=0.6180 +⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.48843129 + text_encoder-item0.clip_prompt_embeds 0.00020838 56.53838187 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.47051573 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.12467978 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00338976 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00675961 1.20674467 + vae.encoder_f1 0.00676652 1.20804262 + vae.decoder 0.00021373 0.03650633 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 4.28162614 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 239624 +BPFP 0.8479 bits/point +EBPFP 1.6957 equivalent bits/point +MSE 4.281626 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.007s, Pack+Encode: 2.136s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2816 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,864B, BPFP=1.1991 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,076B, BPFP=1.3049 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,008B, BPFP=1.4207 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,676B, BPFP=0.3613 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,672B, BPFP=0.3612 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,256B, BPFP=0.9233 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.41308081 + text_encoder-item0.clip_prompt_embeds 0.00021387 144.34924581 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.46680384 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.10196477 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00226559 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00596338 0.65007436 + vae.encoder_f1 0.00596322 0.65012431 + vae.decoder 0.00018207 0.05546455 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 6.32087778 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 222000 +BPFP 0.7855 bits/point +EBPFP 1.5710 equivalent bits/point +MSE 6.320878 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3209 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,936B, BPFP=1.2089 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,744B, BPFP=10.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,356B, BPFP=1.2464 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,004B, BPFP=1.2430 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,148B, BPFP=0.2617 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,144B, BPFP=0.2616 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,104B, BPFP=0.7051 +⌛️ [2/4] FRONTEND: Frontend time: 2.159s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.42952943 + text_encoder-item0.clip_prompt_embeds 0.00022138 34.73861734 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.45657630 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.16979065 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00314001 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00552804 0.54357040 + vae.encoder_f1 0.00552758 0.54345965 + vae.decoder 0.00018040 0.04462553 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 3.40641500 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 194136 +BPFP 0.6869 bits/point +EBPFP 1.3738 equivalent bits/point +MSE 3.406415 +---------------------- -------------------------------------------------------- +Time: 3.774s Load: 0.008s, Pack+Encode: 2.159s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4064 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,960B, BPFP=1.0768 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,940B, BPFP=1.2127 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,892B, BPFP=1.2148 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,792B, BPFP=0.3173 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,784B, BPFP=0.3171 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,696B, BPFP=0.4180 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.49806333 + text_encoder-item0.clip_prompt_embeds 0.00024507 131.25826062 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.48155394 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.10683109 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00308736 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00721525 0.80921811 + vae.encoder_f1 0.00721777 0.81056005 + vae.decoder 0.00018707 0.02556613 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 6.04948736 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 189616 +BPFP 0.6709 bits/point +EBPFP 1.3418 equivalent bits/point +MSE 6.049487 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.007s, Pack+Encode: 2.145s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.0495 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,824B, BPFP=1.0584 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,872B, BPFP=11.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,956B, BPFP=1.1328 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,440B, BPFP=1.1526 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,356B, BPFP=0.4785 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,360B, BPFP=0.4785 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,156B, BPFP=0.5236 +⌛️ [2/4] FRONTEND: Frontend time: 2.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.43983893 + text_encoder-item0.clip_prompt_embeds 0.00046272 336.19653003 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.47908430 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.09667564 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00513437 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01999603 1.28922582 + vae.encoder_f1 0.01999529 1.29137456 + vae.decoder 0.00024882 0.03272383 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 11.63307644 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210676 +BPFP 0.7454 bits/point +EBPFP 1.4909 equivalent bits/point +MSE 11.633076 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.132s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.6331 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,212B, BPFP=1.1109 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,820B, BPFP=11.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,008B, BPFP=1.2182 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,636B, BPFP=1.1576 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,840B, BPFP=0.5164 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,820B, BPFP=0.5161 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,096B, BPFP=0.3386 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.48991072 + text_encoder-item0.clip_prompt_embeds 0.00020334 35.08229970 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.42083349 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.08843431 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00317343 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01341345 1.16567910 + vae.encoder_f1 0.01341645 1.16399813 + vae.decoder 0.00018350 0.01995397 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 3.69715207 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 211116 +BPFP 0.7470 bits/point +EBPFP 1.4940 equivalent bits/point +MSE 3.697152 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6972 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,164B, BPFP=12.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,404B, BPFP=1.1369 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,780B, BPFP=11.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,840B, BPFP=1.2857 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,468B, BPFP=1.3562 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,116B, BPFP=0.5511 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,120B, BPFP=0.5511 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,260B, BPFP=0.7404 +⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.46381307 + text_encoder-item0.clip_prompt_embeds 0.00022316 23.60588939 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.49840260 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.09470883 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00271978 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00606298 0.88847500 + vae.encoder_f1 0.00607096 0.88867062 + vae.decoder 0.00023408 0.04086396 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 3.27153417 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 237668 +BPFP 0.8409 bits/point +EBPFP 1.6819 equivalent bits/point +MSE 3.271534 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.008s, Pack+Encode: 2.135s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2715 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,284B, BPFP=1.1207 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,808B, BPFP=11.3000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,276B, BPFP=1.1588 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,836B, BPFP=1.2895 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,388B, BPFP=0.5095 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,416B, BPFP=0.5099 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,428B, BPFP=0.6539 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.46824153 + text_encoder-item0.clip_prompt_embeds 0.00023597 34.81999755 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.47321715 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.11854504 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00274981 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00653100 0.94888037 + vae.encoder_f1 0.00653745 0.94669819 + vae.decoder 0.00020026 0.03457212 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 3.59260166 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 225120 +BPFP 0.7965 bits/point +EBPFP 1.5931 equivalent bits/point +MSE 3.592602 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5926 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,076B, BPFP=1.2278 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,828B, BPFP=11.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,600B, BPFP=1.2662 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,368B, BPFP=1.2269 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,848B, BPFP=0.5165 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,856B, BPFP=0.5166 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,788B, BPFP=0.4208 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.44582136 + text_encoder-item0.clip_prompt_embeds 0.00022433 34.72551618 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.49489455 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.08942841 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00343056 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00869686 1.24400592 + vae.encoder_f1 0.00870063 1.24112296 + vae.decoder 0.00021246 0.02777013 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 3.72487957 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 218080 +BPFP 0.7716 bits/point +EBPFP 1.5433 equivalent bits/point +MSE 3.724880 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7249 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,188B, BPFP=12.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,384B, BPFP=1.2695 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,768B, BPFP=1.3610 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,536B, BPFP=1.3326 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,608B, BPFP=0.5891 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,616B, BPFP=0.5892 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,504B, BPFP=0.5037 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.43785910 + text_encoder-item0.clip_prompt_embeds 0.00022433 179.89366883 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.46862249 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.29325982 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00250485 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00626512 1.13314390 + vae.encoder_f1 0.00626949 1.13413537 + vae.decoder 0.00018936 0.02926093 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 7.48013243 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 235880 +BPFP 0.8346 bits/point +EBPFP 1.6692 equivalent bits/point +MSE 7.480132 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.4801 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,588B, BPFP=1.2971 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,712B, BPFP=10.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,148B, BPFP=1.3919 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,028B, BPFP=1.3451 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,256B, BPFP=0.4159 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,256B, BPFP=0.4159 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,688B, BPFP=0.3567 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.53210437 + text_encoder-item0.clip_prompt_embeds 0.00026137 59.39752858 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.47233434 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.10789314 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00282507 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.35915655 2.19322610 + vae.encoder_f1 0.35915723 2.22092509 + vae.decoder 0.00024181 0.02469490 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 4.81786333 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 209376 +BPFP 0.7408 bits/point +EBPFP 1.4817 equivalent bits/point +MSE 4.817863 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8179 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,812B, BPFP=1.0568 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,812B, BPFP=11.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,976B, BPFP=1.1344 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,428B, BPFP=1.1523 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,968B, BPFP=0.1826 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,968B, BPFP=0.1826 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,108B, BPFP=0.4000 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.586s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.48218707 + text_encoder-item0.clip_prompt_embeds 0.00021656 34.75330594 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.49520044 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.08540009 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00352582 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.29031765 1.73599136 + vae.encoder_f1 0.29031771 1.73514104 + vae.decoder 0.00019965 0.03380304 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 3.95479459 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 167772 +BPFP 0.5936 bits/point +EBPFP 1.1872 equivalent bits/point +MSE 3.954795 +---------------------- -------------------------------------------------------- +Time: 3.721s Load: 0.007s, Pack+Encode: 2.128s, Decode+Unpack: 1.586s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9548 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,356B, BPFP=0.9951 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,924B, BPFP=1.1302 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,428B, BPFP=1.1523 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,780B, BPFP=0.4391 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,776B, BPFP=0.4391 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,924B, BPFP=0.8217 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.48201636 + text_encoder-item0.clip_prompt_embeds 0.00025451 23.73525433 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.49632621 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.09935313 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00311966 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00595764 0.69722354 + vae.encoder_f1 0.00596395 0.69603091 + vae.decoder 0.00019845 0.04389266 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 3.18651384 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 214616 +BPFP 0.7594 bits/point +EBPFP 1.5187 equivalent bits/point +MSE 3.186514 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1865 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,288B, BPFP=1.2565 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,748B, BPFP=10.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,156B, BPFP=1.3925 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,760B, BPFP=1.3383 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,760B, BPFP=0.3320 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,764B, BPFP=0.3321 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,224B, BPFP=0.3425 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.46952617 + text_encoder-item0.clip_prompt_embeds 0.00026157 35.95517536 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.48033566 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.09779166 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00272213 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.40456498 2.31902456 + vae.encoder_f1 0.40456539 2.31709075 + vae.decoder 0.00020503 0.02396019 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 4.25564446 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 197384 +BPFP 0.6984 bits/point +EBPFP 1.3968 equivalent bits/point +MSE 4.255644 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.007s, Pack+Encode: 2.128s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2556 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,244B, BPFP=1.2505 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,764B, BPFP=11.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,148B, BPFP=1.1484 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,248B, BPFP=1.2238 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,376B, BPFP=0.5245 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,364B, BPFP=0.5244 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,032B, BPFP=0.7029 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.46091505 + text_encoder-item0.clip_prompt_embeds 0.00027179 34.77481991 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.51314864 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.08976622 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00327656 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00673531 1.21197367 + vae.encoder_f1 0.00673732 1.20977080 + vae.decoder 0.00020129 0.03848381 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 3.71272214 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 226864 +BPFP 0.8027 bits/point +EBPFP 1.6054 equivalent bits/point +MSE 3.712722 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7127 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,524B, BPFP=1.2884 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,944B, BPFP=1.2942 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,864B, BPFP=1.3916 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,920B, BPFP=0.4260 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,912B, BPFP=0.4259 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,928B, BPFP=0.3030 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.48261885 + text_encoder-item0.clip_prompt_embeds 0.00023057 59.80302185 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.51540279 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.10542613 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00299536 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00881784 1.15259266 + vae.encoder_f1 0.00882136 1.15527296 + vae.decoder 0.00017598 0.02095719 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 4.33954534 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 209584 +BPFP 0.7416 bits/point +EBPFP 1.4831 equivalent bits/point +MSE 4.339545 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.007s, Pack+Encode: 2.130s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.3395 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,748B, BPFP=1.1834 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,688B, BPFP=10.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,628B, BPFP=1.2685 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,880B, BPFP=1.2906 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,512B, BPFP=0.4656 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,508B, BPFP=0.4655 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,348B, BPFP=0.7430 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.45275712 + text_encoder-item0.clip_prompt_embeds 0.00025208 23.93406512 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.52303133 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.10992198 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00270962 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00582247 0.73408622 + vae.encoder_f1 0.00582996 0.73356199 + vae.decoder 0.00016099 0.04238294 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 3.20919810 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224036 +BPFP 0.7927 bits/point +EBPFP 1.5854 equivalent bits/point +MSE 3.209198 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2092 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,264B, BPFP=1.1180 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,720B, BPFP=10.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,328B, BPFP=1.2442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,584B, BPFP=1.2323 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,144B, BPFP=0.5820 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,156B, BPFP=0.5822 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,456B, BPFP=0.5632 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.42088727 + text_encoder-item0.clip_prompt_embeds 0.00020809 23.86758278 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.50382748 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.11414728 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00243520 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00602745 1.08295894 + vae.encoder_f1 0.00603159 1.08574879 + vae.decoder 0.00017526 0.03373282 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 3.36914508 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 230396 +BPFP 0.8152 bits/point +EBPFP 1.6304 equivalent bits/point +MSE 3.369145 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3691 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,312B, BPFP=1.1245 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,768B, BPFP=11.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,764B, BPFP=1.1984 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,440B, BPFP=1.2287 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,748B, BPFP=0.6065 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,724B, BPFP=0.6061 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,268B, BPFP=0.6490 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.44002930 + text_encoder-item0.clip_prompt_embeds 0.00020908 23.85288149 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.50306969 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.10367761 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00257393 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00634616 1.06853509 + vae.encoder_f1 0.00635208 1.07024515 + vae.decoder 0.00022721 0.03391566 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 3.36141110 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 235732 +BPFP 0.8341 bits/point +EBPFP 1.6682 equivalent bits/point +MSE 3.361411 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3614 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,316B, BPFP=1.1250 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,836B, BPFP=11.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,968B, BPFP=1.1338 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,812B, BPFP=1.1620 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,720B, BPFP=0.2704 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,704B, BPFP=0.2701 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,992B, BPFP=0.2439 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.47290079 + text_encoder-item0.clip_prompt_embeds 0.00022947 24.10580907 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.52258101 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.15278383 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00496201 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.05448642 1.19574833 + vae.encoder_f1 0.05448771 1.19766414 + vae.decoder 0.00017748 0.01979459 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 3.42793027 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 175072 +BPFP 0.6195 bits/point +EBPFP 1.2389 equivalent bits/point +MSE 3.427930 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.007s, Pack+Encode: 2.134s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4279 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,180B, BPFP=12.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,152B, BPFP=1.1028 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,812B, BPFP=11.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,368B, BPFP=1.2474 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,192B, BPFP=1.2731 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,784B, BPFP=0.4087 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,796B, BPFP=0.4089 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,404B, BPFP=0.2870 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.47195554 + text_encoder-item0.clip_prompt_embeds 0.00020169 33.86523226 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.46855578 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.09440441 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00279612 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.06876971 1.42154908 + vae.encoder_f1 0.06877109 1.43146312 + vae.decoder 0.00023999 0.01746751 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 3.78661309 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 200204 +BPFP 0.7084 bits/point +EBPFP 1.4168 equivalent bits/point +MSE 3.786613 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7866 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,772B, BPFP=1.1867 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,804B, BPFP=11.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,332B, BPFP=1.2445 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,756B, BPFP=1.2113 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,396B, BPFP=0.4333 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,376B, BPFP=0.4330 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,272B, BPFP=0.7712 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.46611456 + text_encoder-item0.clip_prompt_embeds 0.00025253 23.79389035 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.50022287 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.11025610 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00290557 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00595097 0.70629799 + vae.encoder_f1 0.00595882 0.70663691 + vae.decoder 0.00020134 0.04784479 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 3.19351148 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 217396 +BPFP 0.7692 bits/point +EBPFP 1.5384 equivalent bits/point +MSE 3.193511 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1935 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,132B, BPFP=11.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,804B, BPFP=1.1910 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,196B, BPFP=1.3146 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,196B, BPFP=1.2479 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,252B, BPFP=0.3395 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,236B, BPFP=0.3393 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,232B, BPFP=0.5564 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.48759758 + text_encoder-item0.clip_prompt_embeds 0.00022201 34.77963508 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.47859817 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.09734143 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00302842 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00831743 0.93090308 + vae.encoder_f1 0.00831926 0.93169475 + vae.decoder 0.00028593 0.02980683 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 3.58246997 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 200388 +BPFP 0.7090 bits/point +EBPFP 1.4181 equivalent bits/point +MSE 3.582470 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5825 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,876B, BPFP=1.3360 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,260B, BPFP=1.3198 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,508B, BPFP=1.3572 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,332B, BPFP=0.5849 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,352B, BPFP=0.5852 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,312B, BPFP=0.5283 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.611s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.45374699 + text_encoder-item0.clip_prompt_embeds 0.00026808 23.82098679 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.48830533 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.09407694 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00280950 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00606586 1.10265100 + vae.encoder_f1 0.00607066 1.10094726 + vae.decoder 0.00019664 0.03193163 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 3.37498779 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 237148 +BPFP 0.8391 bits/point +EBPFP 1.6782 equivalent bits/point +MSE 3.374988 +---------------------- -------------------------------------------------------- +Time: 3.767s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.611s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3750 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,132B, BPFP=11.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,508B, BPFP=1.2863 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,244B, BPFP=1.3997 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,404B, BPFP=1.3800 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,020B, BPFP=0.4428 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,020B, BPFP=0.4428 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,380B, BPFP=0.5304 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.46789145 + text_encoder-item0.clip_prompt_embeds 0.00023198 23.84586166 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.54819193 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.10022220 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00270782 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.05216765 1.44677329 + vae.encoder_f1 0.05216896 1.44530165 + vae.decoder 0.00017960 0.02846958 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 3.53517616 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 220000 +BPFP 0.7784 bits/point +EBPFP 1.5568 equivalent bits/point +MSE 3.535176 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5352 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,160B, BPFP=12.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,896B, BPFP=1.2035 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,208B, BPFP=1.2344 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,600B, BPFP=1.2074 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,396B, BPFP=0.6164 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,416B, BPFP=0.6167 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,264B, BPFP=0.5879 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.613s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.49481277 + text_encoder-item0.clip_prompt_embeds 0.00023125 34.66390397 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.48770928 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.14747149 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00422772 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00620361 0.98234069 + vae.encoder_f1 0.00620966 0.97926474 + vae.decoder 0.00020748 0.03399219 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 3.60524672 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 235296 +BPFP 0.8325 bits/point +EBPFP 1.6651 equivalent bits/point +MSE 3.605247 +---------------------- -------------------------------------------------------- +Time: 3.770s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.613s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6052 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,556B, BPFP=1.1575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,680B, BPFP=10.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,888B, BPFP=1.2896 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,880B, BPFP=1.2652 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,064B, BPFP=0.5198 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,052B, BPFP=0.5196 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,776B, BPFP=0.6035 +⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.43530357 + text_encoder-item0.clip_prompt_embeds 0.00023066 23.82797069 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.50706682 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.13544794 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00416433 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.03159856 1.13474727 + vae.encoder_f1 0.03160188 1.13063097 + vae.decoder 0.00018417 0.02997318 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 3.39126594 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 225604 +BPFP 0.7982 bits/point +EBPFP 1.5965 equivalent bits/point +MSE 3.391266 +---------------------- -------------------------------------------------------- +Time: 3.765s Load: 0.008s, Pack+Encode: 2.163s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3913 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,828B, BPFP=1.1943 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,720B, BPFP=10.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,788B, BPFP=1.2815 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,224B, BPFP=1.3247 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,568B, BPFP=0.6190 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,564B, BPFP=0.6190 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,996B, BPFP=0.5492 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.41214633 + text_encoder-item0.clip_prompt_embeds 0.00024948 35.02747269 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.48484340 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.09196978 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00335758 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.03490865 1.60861421 + vae.encoder_f1 0.03491008 1.61987841 + vae.decoder 0.00028462 0.03679348 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 3.90628107 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 239400 +BPFP 0.8471 bits/point +EBPFP 1.6941 equivalent bits/point +MSE 3.906281 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9063 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,932B, BPFP=1.0731 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,724B, BPFP=10.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,740B, BPFP=1.2776 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,856B, BPFP=1.3407 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,348B, BPFP=0.3105 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,352B, BPFP=0.3105 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,680B, BPFP=0.7532 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.42381056 + text_encoder-item0.clip_prompt_embeds 0.00021560 349.68892045 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.47387180 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.09475118 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00244395 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00544735 0.55522126 + vae.encoder_f1 0.00544843 0.55595660 + vae.decoder 0.00018632 0.04377482 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 11.64604648 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 205356 +BPFP 0.7266 bits/point +EBPFP 1.4532 equivalent bits/point +MSE 11.646046 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.6460 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,140B, BPFP=11.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,192B, BPFP=1.1082 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,780B, BPFP=11.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,540B, BPFP=1.2614 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,780B, BPFP=1.2120 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,740B, BPFP=0.5453 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,740B, BPFP=0.5453 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,728B, BPFP=0.6021 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.47792077 + text_encoder-item0.clip_prompt_embeds 0.00022698 45.64932951 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.51886582 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.13199488 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00404060 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00630479 0.88657945 + vae.encoder_f1 0.00631430 0.88421100 + vae.decoder 0.00018596 0.03012466 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 3.84718512 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 226156 +BPFP 0.8002 bits/point +EBPFP 1.6004 equivalent bits/point +MSE 3.847185 +---------------------- -------------------------------------------------------- +Time: 3.765s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8472 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,740B, BPFP=1.1824 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,856B, BPFP=11.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,912B, BPFP=1.2104 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,408B, BPFP=1.1518 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,880B, BPFP=0.5170 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,876B, BPFP=0.5169 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,620B, BPFP=0.6903 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.48479108 + text_encoder-item0.clip_prompt_embeds 0.00024643 34.77988873 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.51410813 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.12929769 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00270532 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00612578 0.83138579 + vae.encoder_f1 0.00613243 0.83244944 + vae.decoder 0.00018179 0.03444801 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 3.53829193 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 223036 +BPFP 0.7892 bits/point +EBPFP 1.5783 equivalent bits/point +MSE 3.538292 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5383 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,176B, BPFP=1.3766 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,776B, BPFP=1.5240 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,952B, BPFP=1.3939 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,836B, BPFP=0.1501 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,836B, BPFP=0.1501 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,980B, BPFP=0.7623 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.47955263 + text_encoder-item0.clip_prompt_embeds 0.00024049 23.67842135 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.50438967 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.11093798 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00280559 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00526071 0.31135285 + vae.encoder_f1 0.00526072 0.31135055 + vae.decoder 0.00016981 0.04128557 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 3.00651153 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 192068 +BPFP 0.6796 bits/point +EBPFP 1.3592 equivalent bits/point +MSE 3.006512 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0065 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,124B, BPFP=11.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,324B, BPFP=1.1261 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,764B, BPFP=11.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,676B, BPFP=1.1912 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,076B, BPFP=1.2448 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,032B, BPFP=0.5498 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,020B, BPFP=0.5496 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,864B, BPFP=0.6672 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.44734629 + text_encoder-item0.clip_prompt_embeds 0.00022843 23.83870443 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.47309227 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.10807995 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00303183 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00622977 0.91028476 + vae.encoder_f1 0.00623684 0.90973032 + vae.decoder 0.00019755 0.03305928 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 3.28726574 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 229396 +BPFP 0.8117 bits/point +EBPFP 1.6233 equivalent bits/point +MSE 3.287266 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2873 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,392B, BPFP=1.2706 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,880B, BPFP=11.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,704B, BPFP=1.2747 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,404B, BPFP=1.2278 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,392B, BPFP=0.3722 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,384B, BPFP=0.3721 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,584B, BPFP=0.4451 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.50727562 + text_encoder-item0.clip_prompt_embeds 0.00026004 34.70789156 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.51953983 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.11134584 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00351177 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00725303 0.78999346 + vae.encoder_f1 0.00725507 0.78997922 + vae.decoder 0.00017991 0.02579440 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 3.51529981 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 200464 +BPFP 0.7093 bits/point +EBPFP 1.4186 equivalent bits/point +MSE 3.515300 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5153 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,220B, BPFP=12.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,380B, BPFP=1.2689 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,816B, BPFP=11.3500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,960B, BPFP=1.2955 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,940B, BPFP=1.2921 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,052B, BPFP=0.3975 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,044B, BPFP=0.3974 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,100B, BPFP=0.3387 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.47115993 + text_encoder-item0.clip_prompt_embeds 0.00031748 23.72513993 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.50960083 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.11187674 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00315772 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.42111695 2.27037644 + vae.encoder_f1 0.42111716 2.26497936 + vae.decoder 0.00019827 0.02308819 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 3.91299546 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 203028 +BPFP 0.7184 bits/point +EBPFP 1.4367 equivalent bits/point +MSE 3.912995 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9130 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,344B, BPFP=1.2641 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,712B, BPFP=10.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,152B, BPFP=1.3110 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 55,328B, BPFP=1.4034 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,996B, BPFP=0.4577 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,996B, BPFP=0.4577 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,596B, BPFP=0.4149 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.41948994 + text_encoder-item0.clip_prompt_embeds 0.00024951 72.18056683 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.47420254 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.09158004 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00290676 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.10376993 2.15533853 + vae.encoder_f1 0.10377157 2.15394902 + vae.decoder 0.00019787 0.02561623 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 5.12725600 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 217824 +BPFP 0.7707 bits/point +EBPFP 1.5414 equivalent bits/point +MSE 5.127256 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.1273 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,500B, BPFP=1.1499 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,688B, BPFP=10.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,880B, BPFP=1.2890 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,236B, BPFP=1.2489 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,552B, BPFP=0.4814 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,552B, BPFP=0.4814 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,104B, BPFP=0.3999 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.44094225 + text_encoder-item0.clip_prompt_embeds 0.00022350 106.33575149 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.49575438 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.10747732 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00325336 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01346414 1.25223064 + vae.encoder_f1 0.01346933 1.24898028 + vae.decoder 0.00019243 0.02397664 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 5.60188773 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 213232 +BPFP 0.7545 bits/point +EBPFP 1.5089 equivalent bits/point +MSE 5.601888 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6019 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,188B, BPFP=12.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,664B, BPFP=1.1721 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,844B, BPFP=11.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,616B, BPFP=1.1864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 55,008B, BPFP=1.3953 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,360B, BPFP=0.4175 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,372B, BPFP=0.4177 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,164B, BPFP=0.3712 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.44787546 + text_encoder-item0.clip_prompt_embeds 0.00024958 34.73244090 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.54004292 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.09536556 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00350623 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.11196710 1.72493839 + vae.encoder_f1 0.11196851 1.72006488 + vae.decoder 0.00023459 0.02788842 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 3.94794959 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 208732 +BPFP 0.7386 bits/point +EBPFP 1.4771 equivalent bits/point +MSE 3.947950 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9479 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,724B, BPFP=1.3155 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,768B, BPFP=11.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,592B, BPFP=1.3468 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,368B, BPFP=1.2269 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,248B, BPFP=0.5836 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,244B, BPFP=0.5836 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,528B, BPFP=0.5044 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.47760789 + text_encoder-item0.clip_prompt_embeds 0.00025929 34.71396019 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.51595564 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.17787680 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00330897 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00675017 1.11712241 + vae.encoder_f1 0.00675421 1.11655557 + vae.decoder 0.00023635 0.03419824 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 3.67087652 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 231180 +BPFP 0.8180 bits/point +EBPFP 1.6360 equivalent bits/point +MSE 3.670877 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6709 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,348B, BPFP=1.1293 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,112B, BPFP=1.2266 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,020B, BPFP=1.3449 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,980B, BPFP=0.6711 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,996B, BPFP=0.6713 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,508B, BPFP=0.5038 +⌛️ [2/4] FRONTEND: Frontend time: 2.133s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.42586303 + text_encoder-item0.clip_prompt_embeds 0.00064775 192.95038555 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.49222889 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.09326132 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00272250 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00728993 1.37458038 + vae.encoder_f1 0.00729572 1.37443697 + vae.decoder 0.00026488 0.03423963 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 7.92523547 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 244508 +BPFP 0.8651 bits/point +EBPFP 1.7303 equivalent bits/point +MSE 7.925235 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.009s, Pack+Encode: 2.133s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.9252 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,120B, BPFP=1.2338 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,800B, BPFP=11.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,288B, BPFP=1.2409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,928B, BPFP=1.3172 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,148B, BPFP=0.5668 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,160B, BPFP=0.5670 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,588B, BPFP=0.5367 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.44859211 + text_encoder-item0.clip_prompt_embeds 0.00023188 23.78874332 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.47017231 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.09324383 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00286183 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00613207 1.03075445 + vae.encoder_f1 0.00613899 1.02907562 + vae.decoder 0.00023812 0.03482801 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 3.34110169 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 231756 +BPFP 0.8200 bits/point +EBPFP 1.6400 equivalent bits/point +MSE 3.341102 +---------------------- -------------------------------------------------------- +Time: 3.735s Load: 0.009s, Pack+Encode: 2.134s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3411 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,560B, BPFP=1.2933 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,012B, BPFP=1.2997 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,200B, BPFP=1.2733 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,044B, BPFP=0.4890 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,044B, BPFP=0.4890 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,632B, BPFP=0.5076 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.41998323 + text_encoder-item0.clip_prompt_embeds 0.00023678 34.71757897 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.52507277 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.12367758 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00346832 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00636537 1.06179225 + vae.encoder_f1 0.00636991 1.05755377 + vae.decoder 0.00025538 0.03123634 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 3.64176119 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 219996 +BPFP 0.7784 bits/point +EBPFP 1.5568 equivalent bits/point +MSE 3.641761 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.007s, Pack+Encode: 2.143s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6418 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,144B, BPFP=11.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,916B, BPFP=1.2062 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,772B, BPFP=11.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,072B, BPFP=1.1422 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,748B, BPFP=1.1858 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,424B, BPFP=0.4642 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,428B, BPFP=0.4643 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,500B, BPFP=0.3510 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.50685195 + text_encoder-item0.clip_prompt_embeds 0.00023432 240.53578193 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.44241829 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.10296890 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00366683 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.23155926 2.34172797 + vae.encoder_f1 0.23156048 2.34168983 + vae.decoder 0.00018572 0.02446380 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 9.61780374 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 205520 +BPFP 0.7272 bits/point +EBPFP 1.4544 equivalent bits/point +MSE 9.617804 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.6178 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,316B, BPFP=1.1250 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,540B, BPFP=1.1802 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,628B, BPFP=1.2335 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,040B, BPFP=0.5957 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,028B, BPFP=0.5955 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,264B, BPFP=0.6184 +⌛️ [2/4] FRONTEND: Frontend time: 2.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.48705522 + text_encoder-item0.clip_prompt_embeds 0.00022528 23.73543823 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.48944898 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.10558053 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00340376 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00729824 1.09825361 + vae.encoder_f1 0.00730369 1.09894931 + vae.decoder 0.00019938 0.03905215 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 3.37268918 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 233300 +BPFP 0.8255 bits/point +EBPFP 1.6510 equivalent bits/point +MSE 3.372689 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.132s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3727 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,192B, BPFP=1.1082 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,784B, BPFP=11.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,204B, BPFP=1.2341 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,852B, BPFP=1.2138 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,632B, BPFP=0.3911 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,628B, BPFP=0.3911 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,156B, BPFP=0.7372 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.44232086 + text_encoder-item0.clip_prompt_embeds 0.00022149 59.09413048 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.49665632 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.12555184 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00276202 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00564371 0.76466203 + vae.encoder_f1 0.00565042 0.76463389 + vae.decoder 0.00019980 0.04302636 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 4.14384567 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210148 +BPFP 0.7436 bits/point +EBPFP 1.4871 equivalent bits/point +MSE 4.143846 +---------------------- -------------------------------------------------------- +Time: 3.732s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1438 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,160B, BPFP=12.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,992B, BPFP=1.2165 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,000B, BPFP=1.2987 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,924B, BPFP=1.2410 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,176B, BPFP=0.4299 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,168B, BPFP=0.4298 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,984B, BPFP=0.7014 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.620s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.46130832 + text_encoder-item0.clip_prompt_embeds 0.00022173 23.75782730 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.51225328 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.10191623 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00282104 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00576096 0.69161808 + vae.encoder_f1 0.00576981 0.69123167 + vae.decoder 0.00019592 0.03937647 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 3.18423999 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 216708 +BPFP 0.7668 bits/point +EBPFP 1.5335 equivalent bits/point +MSE 3.184240 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.128s, Decode+Unpack: 1.620s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1842 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,104B, BPFP=1.3669 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,052B, BPFP=1.3841 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 55,708B, BPFP=1.4130 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,984B, BPFP=0.3507 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,988B, BPFP=0.3508 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,476B, BPFP=0.4418 +⌛️ [2/4] FRONTEND: Frontend time: 2.167s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.46090110 + text_encoder-item0.clip_prompt_embeds 0.00025917 34.81185741 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.52453694 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.11115699 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00279740 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00594818 0.76600564 + vae.encoder_f1 0.00595328 0.76770079 + vae.decoder 0.00023462 0.02941078 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 3.50758910 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 206788 +BPFP 0.7317 bits/point +EBPFP 1.4633 equivalent bits/point +MSE 3.507589 +---------------------- -------------------------------------------------------- +Time: 3.778s Load: 0.008s, Pack+Encode: 2.167s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5076 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,188B, BPFP=12.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,428B, BPFP=1.4107 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,868B, BPFP=1.5315 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,248B, BPFP=1.3760 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,400B, BPFP=0.3113 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,396B, BPFP=0.3112 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,696B, BPFP=0.2349 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.43409773 + text_encoder-item0.clip_prompt_embeds 0.00022579 45.75819721 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.47870159 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.10026481 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00242116 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.85445058 2.92618895 + vae.encoder_f1 0.85445166 2.93005276 + vae.decoder 0.00025257 0.01451966 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 4.79392760 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 195564 +BPFP 0.6920 bits/point +EBPFP 1.3839 equivalent bits/point +MSE 4.793928 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7939 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,412B, BPFP=1.1380 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,800B, BPFP=11.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,288B, BPFP=1.2409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,468B, BPFP=1.2548 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,300B, BPFP=0.6454 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,284B, BPFP=0.6452 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,744B, BPFP=0.7246 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.44768270 + text_encoder-item0.clip_prompt_embeds 0.00025458 34.74035275 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.49796829 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.15242668 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00287954 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00628510 1.03222215 + vae.encoder_f1 0.00629234 1.03380632 + vae.decoder 0.00023521 0.04193567 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 3.63239900 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 245020 +BPFP 0.8669 bits/point +EBPFP 1.7339 equivalent bits/point +MSE 3.632399 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6324 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,652B, BPFP=1.1705 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,028B, BPFP=1.2198 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,652B, BPFP=1.3102 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,296B, BPFP=0.4470 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,312B, BPFP=0.4473 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,140B, BPFP=0.6146 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.586s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.49119564 + text_encoder-item0.clip_prompt_embeds 0.00022807 34.79762116 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.52520227 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.10594854 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00327070 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00573429 0.73479140 + vae.encoder_f1 0.00574192 0.73415607 + vae.decoder 0.00017875 0.03202675 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 3.49235313 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 217620 +BPFP 0.7700 bits/point +EBPFP 1.5400 equivalent bits/point +MSE 3.492353 +---------------------- -------------------------------------------------------- +Time: 3.736s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.586s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4924 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,244B, BPFP=12.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,096B, BPFP=1.5011 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,792B, BPFP=11.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,180B, BPFP=1.5568 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,388B, BPFP=1.7093 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,980B, BPFP=0.5643 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,976B, BPFP=0.5642 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,988B, BPFP=0.6100 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.45057066 + text_encoder-item0.clip_prompt_embeds 0.00027120 34.72550984 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.47388053 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.10800773 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00238023 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00781570 1.24690151 + vae.encoder_f1 0.00781878 1.25082362 + vae.decoder 0.00029724 0.03901247 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 3.72975684 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 255160 +BPFP 0.9028 bits/point +EBPFP 1.8056 equivalent bits/point +MSE 3.729757 +---------------------- -------------------------------------------------------- +Time: 3.750s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7298 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,236B, BPFP=12.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,904B, BPFP=1.2045 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,720B, BPFP=10.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,316B, BPFP=1.3244 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,792B, BPFP=1.2630 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,612B, BPFP=0.5281 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,624B, BPFP=0.5283 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,040B, BPFP=0.7947 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.48237896 + text_encoder-item0.clip_prompt_embeds 0.00022930 23.77835625 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.46506863 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.10602206 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00317679 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00577752 0.76808870 + vae.encoder_f1 0.00578475 0.76799005 + vae.decoder 0.00024190 0.04097613 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 3.22070280 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 233760 +BPFP 0.8271 bits/point +EBPFP 1.6542 equivalent bits/point +MSE 3.220703 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2207 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,516B, BPFP=1.4226 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,928B, BPFP=12.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,548B, BPFP=1.4244 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,432B, BPFP=1.4314 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,760B, BPFP=0.5457 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,748B, BPFP=0.5455 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,568B, BPFP=0.2920 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.51752468 + text_encoder-item0.clip_prompt_embeds 0.00028764 34.80634047 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.49496937 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.10231559 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00274062 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.03343784 1.58568323 + vae.encoder_f1 0.03344063 1.59815967 + vae.decoder 0.00016139 0.01906416 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 3.88849470 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 229244 +BPFP 0.8111 bits/point +EBPFP 1.6223 equivalent bits/point +MSE 3.888495 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.009s, Pack+Encode: 2.130s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8885 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,156B, BPFP=12.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,016B, BPFP=1.0844 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,756B, BPFP=10.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,004B, BPFP=1.2179 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,028B, BPFP=1.2690 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,124B, BPFP=0.6428 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,112B, BPFP=0.6426 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,500B, BPFP=0.6866 +⌛️ [2/4] FRONTEND: Frontend time: 2.125s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.41618721 + text_encoder-item0.clip_prompt_embeds 0.00023094 36.38594511 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.47194624 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.11565992 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00362324 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00637455 1.05876386 + vae.encoder_f1 0.00637988 1.05708015 + vae.decoder 0.00020059 0.03687311 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 3.68487935 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 243212 +BPFP 0.8605 bits/point +EBPFP 1.7211 equivalent bits/point +MSE 3.684879 +---------------------- -------------------------------------------------------- +Time: 3.729s Load: 0.008s, Pack+Encode: 2.125s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6849 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,192B, BPFP=1.2435 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,852B, BPFP=11.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,920B, BPFP=1.2922 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,060B, BPFP=1.3205 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,468B, BPFP=0.5107 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,472B, BPFP=0.5107 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,924B, BPFP=0.6691 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.48624551 + text_encoder-item0.clip_prompt_embeds 0.00025217 23.78151634 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.50754614 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.08762162 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00271142 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00581597 0.80003428 + vae.encoder_f1 0.00582356 0.80029607 + vae.decoder 0.00019494 0.03627559 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 3.23429773 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 229604 +BPFP 0.8124 bits/point +EBPFP 1.6248 equivalent bits/point +MSE 3.234298 +---------------------- -------------------------------------------------------- +Time: 3.734s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2343 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,144B, BPFP=11.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,612B, BPFP=1.0298 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,832B, BPFP=11.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,296B, BPFP=1.0792 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,960B, BPFP=1.1912 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,632B, BPFP=0.2843 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,636B, BPFP=0.2844 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,012B, BPFP=0.4886 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.52283780 + text_encoder-item0.clip_prompt_embeds 0.00026975 45.69621719 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.48835773 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.08718614 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00401413 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 1.11695218 3.16196227 + vae.encoder_f1 1.11695278 3.15613699 + vae.decoder 0.00019720 0.03228044 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 4.90115084 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 184640 +BPFP 0.6533 bits/point +EBPFP 1.3066 equivalent bits/point +MSE 4.901151 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.007s, Pack+Encode: 2.138s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.9012 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,100B, BPFP=1.2311 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,720B, BPFP=10.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,160B, BPFP=1.2305 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,108B, BPFP=1.3217 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,348B, BPFP=0.5089 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,348B, BPFP=0.5089 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,324B, BPFP=0.5592 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.43361056 + text_encoder-item0.clip_prompt_embeds 0.00025545 34.75243295 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.44552965 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.08970812 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00368267 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01535016 1.31031406 + vae.encoder_f1 0.01535382 1.30726457 + vae.decoder 0.00021460 0.03396961 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 3.75703061 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224820 +BPFP 0.7955 bits/point +EBPFP 1.5909 equivalent bits/point +MSE 3.757031 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.009s, Pack+Encode: 2.139s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7570 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,224B, BPFP=12.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,776B, BPFP=1.3225 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,072B, BPFP=1.3857 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,704B, BPFP=1.3876 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,024B, BPFP=0.4276 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,028B, BPFP=0.4277 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,460B, BPFP=0.8075 +⌛️ [2/4] FRONTEND: Frontend time: 2.169s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.46799791 + text_encoder-item0.clip_prompt_embeds 0.00022628 34.79392840 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.49829984 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.09784645 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00271137 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00589589 0.71368605 + vae.encoder_f1 0.00590398 0.71116972 + vae.decoder 0.00017838 0.04934765 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 3.48358629 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227600 +BPFP 0.8053 bits/point +EBPFP 1.6106 equivalent bits/point +MSE 3.483586 +---------------------- -------------------------------------------------------- +Time: 3.780s Load: 0.009s, Pack+Encode: 2.169s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4836 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,640B, BPFP=1.1688 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,428B, BPFP=1.2523 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,844B, BPFP=1.1882 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,256B, BPFP=0.6143 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,260B, BPFP=0.6143 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,556B, BPFP=0.4137 +⌛️ [2/4] FRONTEND: Frontend time: 2.166s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.41781183 + text_encoder-item0.clip_prompt_embeds 0.00031548 34.71815180 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.50863209 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.11009823 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00517467 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00725484 1.28478658 + vae.encoder_f1 0.00725992 1.28485584 + vae.decoder 0.00019960 0.02746098 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 3.74539104 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 228484 +BPFP 0.8084 bits/point +EBPFP 1.6169 equivalent bits/point +MSE 3.745391 +---------------------- -------------------------------------------------------- +Time: 3.775s Load: 0.009s, Pack+Encode: 2.166s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7454 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,828B, BPFP=1.0590 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,956B, BPFP=12.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,384B, BPFP=1.0864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,252B, BPFP=1.1478 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,392B, BPFP=0.5248 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,372B, BPFP=0.5245 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,476B, BPFP=0.4113 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.47611145 + text_encoder-item0.clip_prompt_embeds 0.00021831 23.77034294 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 0.52192235 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.09446406 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00374355 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00923516 1.15983546 + vae.encoder_f1 0.00923823 1.16510224 + vae.decoder 0.00019521 0.02282491 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 3.40091783 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 212376 +BPFP 0.7514 bits/point +EBPFP 1.5029 equivalent bits/point +MSE 3.400918 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.153s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4009 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,160B, BPFP=1.1039 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,784B, BPFP=1.2812 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,988B, BPFP=1.2933 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,060B, BPFP=0.5502 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,072B, BPFP=0.5504 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,356B, BPFP=0.4991 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.46165419 + text_encoder-item0.clip_prompt_embeds 0.00062166 46.99147727 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.48568587 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.09483017 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00451263 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00831779 1.18807089 + vae.encoder_f1 0.00832197 1.18809283 + vae.decoder 0.00023271 0.02911749 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 4.02096996 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 226904 +BPFP 0.8028 bits/point +EBPFP 1.6057 equivalent bits/point +MSE 4.020970 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.009s, Pack+Encode: 2.143s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0210 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,232B, BPFP=12.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,108B, BPFP=1.2321 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,764B, BPFP=11.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,620B, BPFP=1.1867 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,948B, BPFP=1.3177 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,144B, BPFP=0.4600 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,152B, BPFP=0.4601 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,100B, BPFP=0.4913 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.45685740 + text_encoder-item0.clip_prompt_embeds 0.00022938 23.84818469 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.49875827 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.08870876 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00385352 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00626977 0.88590831 + vae.encoder_f1 0.00627489 0.88549459 + vae.decoder 0.00017842 0.03323586 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 3.27554974 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215584 +BPFP 0.7628 bits/point +EBPFP 1.5256 equivalent bits/point +MSE 3.275550 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2755 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,972B, BPFP=1.2137 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,772B, BPFP=11.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,220B, BPFP=1.3166 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,872B, BPFP=1.2904 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,016B, BPFP=0.5038 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,012B, BPFP=0.5037 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,896B, BPFP=0.7598 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.42580831 + text_encoder-item0.clip_prompt_embeds 0.00022180 23.78970086 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.46295729 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.14772949 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00369611 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00585720 0.81665552 + vae.encoder_f1 0.00586586 0.81700784 + vae.decoder 0.00016520 0.04339989 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 3.24577897 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 230448 +BPFP 0.8154 bits/point +EBPFP 1.6308 equivalent bits/point +MSE 3.245779 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2458 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,176B, BPFP=12.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,592B, BPFP=1.1623 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,628B, BPFP=10.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,352B, BPFP=1.2461 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,496B, BPFP=1.2555 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,112B, BPFP=0.3832 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,116B, BPFP=0.3832 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,860B, BPFP=0.3925 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.47419707 + text_encoder-item0.clip_prompt_embeds 0.00025784 47.11869251 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.42954121 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.09391621 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00309843 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00734802 0.98697448 + vae.encoder_f1 0.00734987 0.98773235 + vae.decoder 0.00018093 0.01959931 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 3.92983761 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 199848 +BPFP 0.7071 bits/point +EBPFP 1.4142 equivalent bits/point +MSE 3.929838 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9298 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,348B, BPFP=1.2646 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,948B, BPFP=1.2945 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,552B, BPFP=1.2569 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,292B, BPFP=0.6148 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,300B, BPFP=0.6149 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,648B, BPFP=0.5996 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.47649972 + text_encoder-item0.clip_prompt_embeds 0.00023510 24.40224229 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.46485271 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.10343945 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00238746 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00637359 1.06608331 + vae.encoder_f1 0.00637830 1.06628251 + vae.decoder 0.00018566 0.03637500 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 3.37453167 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 238528 +BPFP 0.8440 bits/point +EBPFP 1.6880 equivalent bits/point +MSE 3.374532 +---------------------- -------------------------------------------------------- +Time: 3.761s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3745 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,556B, BPFP=1.1575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,780B, BPFP=11.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,952B, BPFP=1.2948 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,916B, BPFP=1.2915 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,000B, BPFP=0.4425 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,992B, BPFP=0.4424 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,352B, BPFP=0.3770 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.45591418 + text_encoder-item0.clip_prompt_embeds 0.00026418 46.82802777 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.47615943 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.10602628 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00321681 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01530954 1.19483137 + vae.encoder_f1 0.01531230 1.19441748 + vae.decoder 0.00017892 0.02313200 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 4.01933517 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 209264 +BPFP 0.7404 bits/point +EBPFP 1.4809 equivalent bits/point +MSE 4.019335 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0193 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,476B, BPFP=1.2819 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,744B, BPFP=10.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,384B, BPFP=1.3299 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,028B, BPFP=1.3704 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,852B, BPFP=0.5623 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,840B, BPFP=0.5621 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,464B, BPFP=0.5940 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.41863004 + text_encoder-item0.clip_prompt_embeds 0.00021481 34.71803977 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.44132261 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.09789161 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00296890 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00591154 1.10813034 + vae.encoder_f1 0.00591973 1.11055601 + vae.decoder 0.00025286 0.03590653 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 3.66410856 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 236512 +BPFP 0.8368 bits/point +EBPFP 1.6737 equivalent bits/point +MSE 3.664109 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6641 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,828B, BPFP=1.3295 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,076B, BPFP=1.3860 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,740B, BPFP=1.4392 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,384B, BPFP=0.3568 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,400B, BPFP=0.3571 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,336B, BPFP=0.6511 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.46257889 + text_encoder-item0.clip_prompt_embeds 0.00023458 23.78372945 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.53026948 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.11905746 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00247829 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00588703 0.62546855 + vae.encoder_f1 0.00589573 0.62680340 + vae.decoder 0.00053402 0.03788324 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 3.15517545 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215212 +BPFP 0.7615 bits/point +EBPFP 1.5230 equivalent bits/point +MSE 3.155175 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.007s, Pack+Encode: 2.142s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1552 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,596B, BPFP=1.2982 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,628B, BPFP=1.3497 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,068B, BPFP=1.3461 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,660B, BPFP=0.5136 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,668B, BPFP=0.5137 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,156B, BPFP=0.4015 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.44462450 + text_encoder-item0.clip_prompt_embeds 0.00022882 278.63230519 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.49260716 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.09785789 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00286367 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00659691 1.06577623 + vae.encoder_f1 0.00660300 1.06645405 + vae.decoder 0.00023739 0.02606568 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 10.02249263 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 223236 +BPFP 0.7899 bits/point +EBPFP 1.5797 equivalent bits/point +MSE 10.022493 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 10.0225 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,180B, BPFP=12.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,972B, BPFP=1.2137 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,968B, BPFP=1.2961 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,164B, BPFP=1.2471 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,780B, BPFP=0.4086 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,780B, BPFP=0.4086 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,708B, BPFP=0.7845 +⌛️ [2/4] FRONTEND: Frontend time: 2.167s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.47913905 + text_encoder-item0.clip_prompt_embeds 0.00023928 34.75040584 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.51477423 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.10694828 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00302770 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00583864 0.67657036 + vae.encoder_f1 0.00583800 0.67657936 + vae.decoder 0.00018889 0.04320321 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 3.46556207 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 216844 +BPFP 0.7673 bits/point +EBPFP 1.5345 equivalent bits/point +MSE 3.465562 +---------------------- -------------------------------------------------------- +Time: 3.777s Load: 0.008s, Pack+Encode: 2.167s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4656 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,584B, BPFP=1.2965 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,720B, BPFP=10.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,612B, BPFP=1.3484 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 58,776B, BPFP=1.4909 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,748B, BPFP=0.3318 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,728B, BPFP=0.3315 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,324B, BPFP=0.6508 +⌛️ [2/4] FRONTEND: Frontend time: 2.170s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.48489928 + text_encoder-item0.clip_prompt_embeds 0.00024821 48.50260417 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.39562666 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.10803685 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00241751 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00570467 0.59137046 + vae.encoder_f1 0.00570488 0.59181213 + vae.decoder 0.00017302 0.03520211 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 3.78480583 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 213192 +BPFP 0.7543 bits/point +EBPFP 1.5087 equivalent bits/point +MSE 3.784806 +---------------------- -------------------------------------------------------- +Time: 3.775s Load: 0.009s, Pack+Encode: 2.170s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7848 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,212B, BPFP=12.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,216B, BPFP=1.2468 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,888B, BPFP=1.2896 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,564B, BPFP=1.2826 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,524B, BPFP=0.3132 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,532B, BPFP=0.3133 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,100B, BPFP=0.3082 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.45486259 + text_encoder-item0.clip_prompt_embeds 0.00021458 191.71054857 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.41207881 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.11936844 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00501951 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00914783 1.10332310 + vae.encoder_f1 0.00914958 1.10326159 + vae.decoder 0.00017527 0.02260429 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 7.76710010 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 190312 +BPFP 0.6734 bits/point +EBPFP 1.3468 equivalent bits/point +MSE 7.767100 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.007s, Pack+Encode: 2.141s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.7671 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,156B, BPFP=12.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,628B, BPFP=1.4378 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,848B, BPFP=11.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,628B, BPFP=1.4308 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,300B, BPFP=1.4281 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,456B, BPFP=0.4037 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,456B, BPFP=0.4037 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,032B, BPFP=0.7944 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.45554968 + text_encoder-item0.clip_prompt_embeds 0.00022150 34.79223950 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.49971581 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.09341963 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00286682 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00578482 0.65561140 + vae.encoder_f1 0.00579739 0.65547901 + vae.decoder 0.00017668 0.04466816 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 3.45644448 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227020 +BPFP 0.8033 bits/point +EBPFP 1.6065 equivalent bits/point +MSE 3.456444 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4564 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,160B, BPFP=12.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,192B, BPFP=1.1082 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,772B, BPFP=11.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,084B, BPFP=1.2244 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,552B, BPFP=1.2315 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,632B, BPFP=0.3911 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,620B, BPFP=0.3909 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,412B, BPFP=0.5619 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.49651615 + text_encoder-item0.clip_prompt_embeds 0.00023894 23.84522753 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.51571798 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.09949506 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00321781 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00958025 0.97719777 + vae.encoder_f1 0.00958229 0.97782356 + vae.decoder 0.00019995 0.03510182 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 3.31867152 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 204940 +BPFP 0.7251 bits/point +EBPFP 1.4503 equivalent bits/point +MSE 3.318672 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3187 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,164B, BPFP=12.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,320B, BPFP=1.3961 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,832B, BPFP=11.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,260B, BPFP=1.5633 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,524B, BPFP=1.4337 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,856B, BPFP=0.3182 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,856B, BPFP=0.3182 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,336B, BPFP=0.8037 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.46653974 + text_encoder-item0.clip_prompt_embeds 0.00023387 311.56486742 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.46180024 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.10085756 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00260697 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00567713 0.60480934 + vae.encoder_f1 0.00567905 0.60489225 + vae.decoder 0.00019376 0.04953445 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 10.67272649 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 217664 +BPFP 0.7702 bits/point +EBPFP 1.5403 equivalent bits/point +MSE 10.672726 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.009s, Pack+Encode: 2.145s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 10.6727 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,952B, BPFP=1.2110 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,768B, BPFP=11.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,692B, BPFP=1.2737 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,344B, BPFP=1.4292 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,508B, BPFP=0.4045 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,508B, BPFP=0.4045 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,860B, BPFP=0.4840 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.42045033 + text_encoder-item0.clip_prompt_embeds 0.00024281 34.81082167 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.46979480 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.09043293 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00239926 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.02387581 0.99439889 + vae.encoder_f1 0.02387858 0.99163628 + vae.decoder 0.00018648 0.02448732 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 3.61087534 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 213356 +BPFP 0.7549 bits/point +EBPFP 1.5098 equivalent bits/point +MSE 3.610875 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6109 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,224B, BPFP=12.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,104B, BPFP=1.2316 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,680B, BPFP=1.2727 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,448B, BPFP=1.2796 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,152B, BPFP=0.6127 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,132B, BPFP=0.6124 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,856B, BPFP=0.3618 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.46035035 + text_encoder-item0.clip_prompt_embeds 0.00022399 34.80298803 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.47728047 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.10038954 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00353762 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01169517 1.41546988 + vae.encoder_f1 0.01169969 1.41704273 + vae.decoder 0.00021186 0.02274050 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 3.80736315 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 230840 +BPFP 0.8168 bits/point +EBPFP 1.6335 equivalent bits/point +MSE 3.807363 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.009s, Pack+Encode: 2.152s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8074 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,892B, BPFP=1.2029 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,692B, BPFP=10.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,732B, BPFP=1.2769 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,172B, BPFP=1.4248 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,500B, BPFP=0.4807 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,496B, BPFP=0.4806 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,668B, BPFP=0.6307 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.44455918 + text_encoder-item0.clip_prompt_embeds 0.00022123 408.72920049 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.47382030 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.09494475 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00282377 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.32749966 2.92837834 + vae.encoder_f1 0.32750070 2.92625999 + vae.decoder 0.00039956 0.03305019 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 14.28899645 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227860 +BPFP 0.8062 bits/point +EBPFP 1.6125 equivalent bits/point +MSE 14.288996 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 14.2890 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,332B, BPFP=1.2624 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,736B, BPFP=10.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,080B, BPFP=1.3052 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,180B, BPFP=1.2221 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,772B, BPFP=0.4085 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,788B, BPFP=0.4088 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,368B, BPFP=0.6216 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.583s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.44690617 + text_encoder-item0.clip_prompt_embeds 0.00024675 34.76230849 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.47967234 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.09793231 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00272321 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00566967 0.70395315 + vae.encoder_f1 0.00567867 0.70340043 + vae.decoder 0.00017839 0.03391865 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 3.47689961 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210956 +BPFP 0.7464 bits/point +EBPFP 1.4928 equivalent bits/point +MSE 3.476900 +---------------------- -------------------------------------------------------- +Time: 3.727s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.583s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4769 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,936B, BPFP=1.0736 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,836B, BPFP=11.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,108B, BPFP=1.1451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,428B, BPFP=1.2791 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,668B, BPFP=0.3917 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,668B, BPFP=0.3917 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,784B, BPFP=0.8479 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.39983439 + text_encoder-item0.clip_prompt_embeds 0.00022364 394.31892587 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.53581328 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.12576789 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00315060 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00580750 0.64619899 + vae.encoder_f1 0.00580664 0.64619195 + vae.decoder 0.00018044 0.05039147 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 12.85760455 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215140 +BPFP 0.7612 bits/point +EBPFP 1.5224 equivalent bits/point +MSE 12.857605 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 12.8576 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,608B, BPFP=1.1645 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,748B, BPFP=10.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,208B, BPFP=1.2344 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,808B, BPFP=1.2127 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,584B, BPFP=0.4362 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,568B, BPFP=0.4359 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,904B, BPFP=0.4548 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.46860909 + text_encoder-item0.clip_prompt_embeds 0.00030118 130.46824269 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.43329935 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.09710002 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00369448 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.03869025 1.45730972 + vae.encoder_f1 0.03869358 1.45887792 + vae.decoder 0.00021614 0.02939026 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 6.32950778 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 207116 +BPFP 0.7328 bits/point +EBPFP 1.4657 equivalent bits/point +MSE 6.329508 +---------------------- -------------------------------------------------------- +Time: 3.762s Load: 0.009s, Pack+Encode: 2.161s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3295 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,464B, BPFP=1.1450 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,876B, BPFP=1.2075 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,256B, BPFP=1.2240 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,756B, BPFP=0.6066 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,752B, BPFP=0.6066 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,344B, BPFP=0.4072 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.47789145 + text_encoder-item0.clip_prompt_embeds 0.00023260 322.23390152 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.51643081 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.12122218 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00537749 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00839879 1.29318142 + vae.encoder_f1 0.00840224 1.29159129 + vae.decoder 0.00019463 0.02794579 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 11.26943693 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227920 +BPFP 0.8064 bits/point +EBPFP 1.6129 equivalent bits/point +MSE 11.269437 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.2694 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,612B, BPFP=1.4356 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,828B, BPFP=1.4471 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,240B, BPFP=1.3251 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,784B, BPFP=0.5613 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,776B, BPFP=0.5612 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,620B, BPFP=0.4156 +⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.44490616 + text_encoder-item0.clip_prompt_embeds 0.00023544 34.72738476 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.48182340 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.10074099 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00310316 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01160815 1.24669755 + vae.encoder_f1 0.01161249 1.24407470 + vae.decoder 0.00021720 0.02532651 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 3.72639348 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 231400 +BPFP 0.8188 bits/point +EBPFP 1.6375 equivalent bits/point +MSE 3.726393 +---------------------- -------------------------------------------------------- +Time: 3.768s Load: 0.009s, Pack+Encode: 2.155s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7264 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,260B, BPFP=13.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,440B, BPFP=1.2771 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,724B, BPFP=10.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,876B, BPFP=1.2886 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,052B, BPFP=1.2949 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,204B, BPFP=0.3541 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,216B, BPFP=0.3542 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,844B, BPFP=0.3615 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.41759515 + text_encoder-item0.clip_prompt_embeds 0.00022923 57.78017620 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.49131541 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.11124294 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00339015 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.02989292 1.41650891 + vae.encoder_f1 0.02989391 1.42024755 + vae.decoder 0.00034944 0.02332539 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 4.40982687 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 198132 +BPFP 0.7010 bits/point +EBPFP 1.4021 equivalent bits/point +MSE 4.409827 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4098 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,800B, BPFP=1.3258 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,860B, BPFP=11.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,896B, BPFP=1.2903 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,432B, BPFP=1.3046 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,392B, BPFP=0.5706 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,396B, BPFP=0.5706 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,044B, BPFP=0.5812 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.587s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.48736111 + text_encoder-item0.clip_prompt_embeds 0.00024627 131.25132745 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.50625057 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.09157033 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00460281 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00613025 0.90441656 + vae.encoder_f1 0.00613536 0.90338576 + vae.decoder 0.00018697 0.03413341 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 6.09345564 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 234540 +BPFP 0.8299 bits/point +EBPFP 1.6597 equivalent bits/point +MSE 6.093456 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.587s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.0935 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.7726 bits/point +Avg EBPFP 1.5452 equivalent bits/point +Avg MSE 4.703740 +Avg Time 3.761s +------------------------ ---------------------------- diff --git a/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log b/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..9d3524a67f478e7d8ded8b1c473d68a25b71fa56 --- /dev/null +++ b/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log @@ -0,0 +1,21862 @@ +Experiment: dtufc_hyperprior-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: sd35 + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 599 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,152B, BPFP=12.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,600B, BPFP=1.1634 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,800B, BPFP=11.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,956B, BPFP=1.2140 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,856B, BPFP=1.2139 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,020B, BPFP=0.6412 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,020B, BPFP=0.6412 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,620B, BPFP=0.6598 +⌛️ [2/4] FRONTEND: Frontend time: 0.692s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.77226011 + text_encoder-item0.clip_prompt_embeds 0.00025464 23.87514796 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.70099015 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.10079690 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00203022 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00635250 1.17163861 + vae.encoder_f1 0.00635834 1.17163885 + vae.decoder 0.00019940 0.03032322 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 1.82371082 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 230880 +BPFP 0.8169 bits/point +EBPFP 1.6338 equivalent bits/point +MSE 1.823711 +---------------------- -------------------------------------------------------- +Time: 1.215s Load: 0.009s, Pack+Encode: 0.692s, Decode+Unpack: 0.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8237 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,220B, BPFP=12.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,472B, BPFP=1.1461 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,812B, BPFP=11.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,972B, BPFP=1.2153 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,616B, BPFP=1.1571 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,784B, BPFP=0.4850 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,784B, BPFP=0.4850 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,872B, BPFP=0.5759 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.68250640 + text_encoder-item0.clip_prompt_embeds 0.00022609 47.89544440 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.73362370 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.10298376 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00185830 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.01130640 1.63679457 + vae.encoder_f1 0.01130902 1.63633537 + vae.decoder 0.00020860 0.03012123 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 2.66761294 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 205388 +BPFP 0.7267 bits/point +EBPFP 1.4534 equivalent bits/point +MSE 2.667613 +---------------------- -------------------------------------------------------- +Time: 0.760s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6676 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,240B, BPFP=12.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,772B, BPFP=1.0514 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 2,048B, BPFP=12.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,648B, BPFP=1.1078 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,112B, BPFP=1.0428 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,536B, BPFP=0.2676 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,536B, BPFP=0.2676 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,784B, BPFP=0.4512 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.71945421 + text_encoder-item0.clip_prompt_embeds 0.00022402 180.10658482 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 0.83271666 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.08599626 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00176421 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 1.19630027 5.51562738 + vae.encoder_f1 1.19630098 5.51563454 + vae.decoder 0.00023596 0.02688876 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 7.92350831 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 166532 +BPFP 0.5892 bits/point +EBPFP 1.1785 equivalent bits/point +MSE 7.923508 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.9235 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,284B, BPFP=1.1207 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,956B, BPFP=1.2140 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,040B, BPFP=1.1425 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,956B, BPFP=0.5944 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,952B, BPFP=0.5944 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 29,716B, BPFP=0.9069 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.72664014 + text_encoder-item0.clip_prompt_embeds 0.00030342 47.94658922 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.72721539 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.09593084 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00166305 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00586287 0.85200727 + vae.encoder_f1 0.00587438 0.85197687 + vae.decoder 0.00017677 0.04428006 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 2.30640902 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 229740 +BPFP 0.8129 bits/point +EBPFP 1.6258 equivalent bits/point +MSE 2.306409 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3064 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,188B, BPFP=12.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,624B, BPFP=1.0314 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,868B, BPFP=11.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,428B, BPFP=1.0899 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,948B, BPFP=1.0387 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,736B, BPFP=0.4232 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,736B, BPFP=0.4232 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,424B, BPFP=0.5317 +⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.70502822 + text_encoder-item0.clip_prompt_embeds 0.00024120 72.73987926 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.75528893 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.12971187 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00157944 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00779453 1.20949602 + vae.encoder_f1 0.00779802 1.20887434 + vae.decoder 0.00023829 0.02944841 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 3.12027945 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188808 +BPFP 0.6681 bits/point +EBPFP 1.3361 equivalent bits/point +MSE 3.120279 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.007s, Pack+Encode: 0.287s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1203 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,192B, BPFP=1.1082 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,832B, BPFP=11.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,188B, BPFP=1.1516 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,052B, BPFP=1.0920 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,692B, BPFP=0.6362 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,696B, BPFP=0.6362 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,996B, BPFP=0.6102 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.72562385 + text_encoder-item0.clip_prompt_embeds 0.00025651 23.88046199 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.75300593 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.09584953 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00160122 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00655775 1.32618380 + vae.encoder_f1 0.00656268 1.32613194 + vae.decoder 0.00020283 0.03100961 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 1.89532854 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 222712 +BPFP 0.7880 bits/point +EBPFP 1.5760 equivalent bits/point +MSE 1.895329 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8953 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,560B, BPFP=1.0227 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,832B, BPFP=11.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,820B, BPFP=1.0406 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,976B, BPFP=0.9886 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,756B, BPFP=0.6219 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,756B, BPFP=0.6219 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,912B, BPFP=0.7908 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.71901846 + text_encoder-item0.clip_prompt_embeds 0.00022242 47.93682359 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.77102575 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.08414150 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00149734 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00593415 0.97717273 + vae.encoder_f1 0.00594307 0.97740453 + vae.decoder 0.00018992 0.04071852 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 2.36333440 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 220676 +BPFP 0.7808 bits/point +EBPFP 1.5616 equivalent bits/point +MSE 2.363334 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3633 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,152B, BPFP=12.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,168B, BPFP=1.1050 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,772B, BPFP=11.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,760B, BPFP=1.1981 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,936B, BPFP=1.0891 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,048B, BPFP=0.5500 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,048B, BPFP=0.5500 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,640B, BPFP=0.5688 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.71801265 + text_encoder-item0.clip_prompt_embeds 0.00022110 47.88626218 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.72832661 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.11233324 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00179140 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00641770 1.09569728 + vae.encoder_f1 0.00642053 1.09562349 + vae.decoder 0.00017498 0.02519346 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 2.41635444 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210380 +BPFP 0.7444 bits/point +EBPFP 1.4888 equivalent bits/point +MSE 2.416354 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4164 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,316B, BPFP=0.9897 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,916B, BPFP=11.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,080B, BPFP=1.0617 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,536B, BPFP=1.1043 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,584B, BPFP=0.4819 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,584B, BPFP=0.4819 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,764B, BPFP=0.7557 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.72408501 + text_encoder-item0.clip_prompt_embeds 0.00021654 143.85054789 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.77154827 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.08882023 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00170598 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00577698 0.87094253 + vae.encoder_f1 0.00578348 0.87072521 + vae.decoder 0.00017559 0.03553892 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 4.82221165 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 205820 +BPFP 0.7282 bits/point +EBPFP 1.4565 equivalent bits/point +MSE 4.822212 +---------------------- -------------------------------------------------------- +Time: 0.742s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.444s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8222 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,760B, BPFP=1.0498 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,700B, BPFP=10.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,004B, BPFP=1.1367 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,868B, BPFP=1.1381 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,888B, BPFP=0.5476 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,888B, BPFP=0.5476 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,596B, BPFP=0.5675 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.76570018 + text_encoder-item0.clip_prompt_embeds 0.00022160 23.88880927 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.67702570 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.09111457 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00189924 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00668450 1.10445583 + vae.encoder_f1 0.00668875 1.10430443 + vae.decoder 0.00023059 0.03141854 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 1.79254660 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210728 +BPFP 0.7456 bits/point +EBPFP 1.4912 equivalent bits/point +MSE 1.792547 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7925 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,240B, BPFP=1.1147 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,716B, BPFP=10.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,312B, BPFP=1.1617 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,412B, BPFP=1.1265 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,936B, BPFP=0.4720 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,936B, BPFP=0.4720 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,840B, BPFP=0.3918 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.72503551 + text_encoder-item0.clip_prompt_embeds 0.00023190 47.97916244 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.66357579 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.09784446 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00196851 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.04018118 2.10300517 + vae.encoder_f1 0.04018488 2.10326338 + vae.decoder 0.00016201 0.02091302 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 2.88488106 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 195452 +BPFP 0.6916 bits/point +EBPFP 1.3831 equivalent bits/point +MSE 2.884881 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8849 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,700B, BPFP=1.0417 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,784B, BPFP=11.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,308B, BPFP=1.0802 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,108B, BPFP=1.0681 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,976B, BPFP=0.5642 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,976B, BPFP=0.5642 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,900B, BPFP=0.6073 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.74260441 + text_encoder-item0.clip_prompt_embeds 0.00023140 84.57973992 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.71835480 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.08839612 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00162851 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.04874706 1.90246129 + vae.encoder_f1 0.04875064 1.90237260 + vae.decoder 0.00019641 0.02643475 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 3.74929678 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210792 +BPFP 0.7458 bits/point +EBPFP 1.4917 equivalent bits/point +MSE 3.749297 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7493 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,956B, BPFP=1.0763 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,768B, BPFP=11.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,684B, BPFP=1.1107 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,600B, BPFP=1.0298 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,532B, BPFP=0.6642 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,528B, BPFP=0.6642 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,824B, BPFP=0.4219 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.69122879 + text_encoder-item0.clip_prompt_embeds 0.00030893 23.87623867 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.73098888 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.09361385 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00150027 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.01360236 1.78130507 + vae.encoder_f1 0.01360807 1.78151393 + vae.decoder 0.00023006 0.02549537 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 2.10557425 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 216956 +BPFP 0.7676 bits/point +EBPFP 1.5353 equivalent bits/point +MSE 2.105574 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1056 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,432B, BPFP=1.1407 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,820B, BPFP=11.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,580B, BPFP=1.1834 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,456B, BPFP=1.1784 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,512B, BPFP=0.2367 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,516B, BPFP=0.2368 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,352B, BPFP=0.2244 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.443s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.70677678 + text_encoder-item0.clip_prompt_embeds 0.00024198 97.42011634 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.73506508 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.09594506 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00164773 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 1.67190456 6.33080149 + vae.encoder_f1 1.67190480 6.33093214 + vae.decoder 0.00017417 0.01363677 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 6.13775449 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 161732 +BPFP 0.5723 bits/point +EBPFP 1.1445 equivalent bits/point +MSE 6.137754 +---------------------- -------------------------------------------------------- +Time: 0.740s Load: 0.007s, Pack+Encode: 0.289s, Decode+Unpack: 0.443s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.1378 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,152B, BPFP=12.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,980B, BPFP=1.0795 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,764B, BPFP=11.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,876B, BPFP=1.1263 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,044B, BPFP=1.1679 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 44,392B, BPFP=0.6774 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 44,396B, BPFP=0.6774 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,816B, BPFP=0.6963 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.71099361 + text_encoder-item0.clip_prompt_embeds 0.00025129 35.93287084 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.74381204 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.09086921 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00212698 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00621760 1.12243056 + vae.encoder_f1 0.00622505 1.12222123 + vae.decoder 0.00025114 0.03508249 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 2.11634550 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 233276 +BPFP 0.8254 bits/point +EBPFP 1.6508 equivalent bits/point +MSE 2.116345 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1163 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,104B, BPFP=11.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,512B, BPFP=1.0162 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,836B, BPFP=11.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,524B, BPFP=1.0977 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,856B, BPFP=1.0363 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 46,900B, BPFP=0.7156 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 46,900B, BPFP=0.7156 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,664B, BPFP=0.6917 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.77592778 + text_encoder-item0.clip_prompt_embeds 0.00020838 23.87663395 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.72420816 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.08806038 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00169832 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00675961 1.46338105 + vae.encoder_f1 0.00676652 1.46365798 + vae.decoder 0.00021373 0.03682376 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 1.95928126 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 232152 +BPFP 0.8214 bits/point +EBPFP 1.6428 equivalent bits/point +MSE 1.959281 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9593 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,964B, BPFP=1.0774 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,360B, BPFP=1.1656 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,580B, BPFP=1.2069 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,080B, BPFP=0.4132 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,080B, BPFP=0.4132 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 35,032B, BPFP=1.0691 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.67920955 + text_encoder-item0.clip_prompt_embeds 0.00021387 23.86461504 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.73122520 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.09726774 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00184469 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00596338 0.63438886 + vae.encoder_f1 0.00596322 0.63444906 + vae.decoder 0.00018207 0.04986699 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 1.57636167 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 212864 +BPFP 0.7532 bits/point +EBPFP 1.5063 equivalent bits/point +MSE 1.576362 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.5764 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,176B, BPFP=12.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,940B, BPFP=1.0741 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,712B, BPFP=1.1130 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,264B, BPFP=1.0213 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,648B, BPFP=0.3151 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,648B, BPFP=0.3151 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,756B, BPFP=0.8470 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.68477082 + text_encoder-item0.clip_prompt_embeds 0.00022138 48.21359240 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.73505754 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.13274220 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00167194 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00552804 0.51601350 + vae.encoder_f1 0.00552758 0.51606226 + vae.decoder 0.00018040 0.04050552 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 2.15874611 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 184788 +BPFP 0.6538 bits/point +EBPFP 1.3077 equivalent bits/point +MSE 2.158746 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1587 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,248B, BPFP=13.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,904B, BPFP=1.0693 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,856B, BPFP=11.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,460B, BPFP=1.0925 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,444B, BPFP=1.0512 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,416B, BPFP=0.3726 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,416B, BPFP=0.3726 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,912B, BPFP=0.4856 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.72471515 + text_encoder-item0.clip_prompt_embeds 0.00024507 24.16884385 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.74504595 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.09638021 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00176170 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00721525 0.97016490 + vae.encoder_f1 0.00721777 0.97030216 + vae.decoder 0.00018707 0.02502372 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 1.73715146 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 181512 +BPFP 0.6422 bits/point +EBPFP 1.2845 equivalent bits/point +MSE 1.737151 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.7372 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,468B, BPFP=1.0103 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,980B, BPFP=12.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,848B, BPFP=1.0429 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,540B, BPFP=0.9776 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,004B, BPFP=0.5341 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,004B, BPFP=0.5341 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,288B, BPFP=0.6191 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.68266996 + text_encoder-item0.clip_prompt_embeds 0.00046272 181.11928436 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.75769472 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.08192522 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00160106 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.01999603 1.80523539 + vae.encoder_f1 0.01999529 1.80563152 + vae.decoder 0.00024882 0.03495341 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 6.23000390 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 203192 +BPFP 0.7189 bits/point +EBPFP 1.4379 equivalent bits/point +MSE 6.230004 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.2300 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,164B, BPFP=12.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,532B, BPFP=1.0189 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,980B, BPFP=1.0536 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,116B, BPFP=0.9922 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,160B, BPFP=0.5670 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,156B, BPFP=0.5670 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,800B, BPFP=0.3906 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.72413357 + text_encoder-item0.clip_prompt_embeds 0.00020334 35.86075741 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.67352362 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.08769123 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00159247 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.01341345 1.64188647 + vae.encoder_f1 0.01341645 1.64050329 + vae.decoder 0.00018350 0.02017561 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 2.35311751 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 200560 +BPFP 0.7096 bits/point +EBPFP 1.4193 equivalent bits/point +MSE 2.353118 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3531 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,728B, BPFP=1.0455 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,832B, BPFP=11.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,948B, BPFP=1.1321 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,444B, BPFP=1.1273 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,568B, BPFP=0.6038 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,568B, BPFP=0.6038 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,908B, BPFP=0.8517 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.70034663 + text_encoder-item0.clip_prompt_embeds 0.00022316 131.95273607 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.73944383 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.09083567 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00195187 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00606298 0.97367847 + vae.encoder_f1 0.00607096 0.97369158 + vae.decoder 0.00023408 0.04068054 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 4.55941681 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227044 +BPFP 0.8033 bits/point +EBPFP 1.6067 equivalent bits/point +MSE 4.559417 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.5594 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,644B, BPFP=1.0341 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,896B, BPFP=1.0468 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,868B, BPFP=1.0874 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,880B, BPFP=0.5627 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,880B, BPFP=0.5627 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,404B, BPFP=0.7448 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.72010438 + text_encoder-item0.clip_prompt_embeds 0.00023597 48.00331862 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.74807510 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.08506088 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00184919 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00653100 1.10710025 + vae.encoder_f1 0.00653745 1.10711658 + vae.decoder 0.00020026 0.03506262 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 2.42470062 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215440 +BPFP 0.7623 bits/point +EBPFP 1.5246 equivalent bits/point +MSE 2.424701 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4247 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,112B, BPFP=11.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,092B, BPFP=1.0947 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,808B, BPFP=11.3000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,656B, BPFP=1.1084 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,396B, BPFP=1.0500 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,660B, BPFP=0.5594 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,660B, BPFP=0.5594 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,752B, BPFP=0.5112 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.73558013 + text_encoder-item0.clip_prompt_embeds 0.00022433 72.23697917 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.76362591 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.09260081 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00156168 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00869686 1.81949973 + vae.encoder_f1 0.00870063 1.81982827 + vae.decoder 0.00021246 0.02801309 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 3.38847524 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 206992 +BPFP 0.7324 bits/point +EBPFP 1.4648 equivalent bits/point +MSE 3.388475 +---------------------- -------------------------------------------------------- +Time: 0.761s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.457s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3885 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,160B, BPFP=12.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,184B, BPFP=1.1071 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,740B, BPFP=10.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,508B, BPFP=1.1776 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,944B, BPFP=1.0893 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,292B, BPFP=0.6606 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,292B, BPFP=0.6606 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,872B, BPFP=0.5759 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.73653587 + text_encoder-item0.clip_prompt_embeds 0.00022433 23.88874586 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.71865273 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.09191470 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00166157 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00626512 1.35122705 + vae.encoder_f1 0.00626949 1.35113406 + vae.decoder 0.00018936 0.02941111 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 1.90678575 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224848 +BPFP 0.7956 bits/point +EBPFP 1.5911 equivalent bits/point +MSE 1.906786 +---------------------- -------------------------------------------------------- +Time: 0.763s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9068 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,148B, BPFP=11.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,192B, BPFP=1.1082 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,700B, BPFP=10.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,480B, BPFP=1.1753 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,608B, BPFP=1.1315 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,320B, BPFP=0.4626 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,324B, BPFP=0.4627 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,168B, BPFP=0.4324 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.78893224 + text_encoder-item0.clip_prompt_embeds 0.00026137 23.89315941 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.71199942 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.10298830 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00186468 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.35915655 3.40474868 + vae.encoder_f1 0.35915723 3.40479159 + vae.decoder 0.00024181 0.02418564 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 2.85920978 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 195796 +BPFP 0.6928 bits/point +EBPFP 1.3856 equivalent bits/point +MSE 2.859210 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8592 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,180B, BPFP=12.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,416B, BPFP=1.0032 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,864B, BPFP=11.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,748B, BPFP=1.0347 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,460B, BPFP=1.0009 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,384B, BPFP=0.2195 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,384B, BPFP=0.2195 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,264B, BPFP=0.4963 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.73459943 + text_encoder-item0.clip_prompt_embeds 0.00021656 60.18539468 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.75845962 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.08065246 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00130663 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.29031765 3.10179353 + vae.encoder_f1 0.29031771 3.10234284 + vae.decoder 0.00019965 0.03296054 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 3.66801947 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 158556 +BPFP 0.5610 bits/point +EBPFP 1.1220 equivalent bits/point +MSE 3.668019 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.007s, Pack+Encode: 0.293s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6680 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,180B, BPFP=12.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,320B, BPFP=0.9903 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,920B, BPFP=1.0487 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,136B, BPFP=0.9673 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,104B, BPFP=0.4899 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,108B, BPFP=0.4899 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,104B, BPFP=0.9187 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.75472633 + text_encoder-item0.clip_prompt_embeds 0.00025451 35.92317708 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.71833334 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.08651767 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00143902 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00595764 0.71618617 + vae.encoder_f1 0.00596395 0.71599078 + vae.decoder 0.00019845 0.04502878 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 1.92855996 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 206504 +BPFP 0.7307 bits/point +EBPFP 1.4613 equivalent bits/point +MSE 1.928560 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9286 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,152B, BPFP=12.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,148B, BPFP=1.1023 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,740B, BPFP=10.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,560B, BPFP=1.1818 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,472B, BPFP=1.1280 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,544B, BPFP=0.3593 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,544B, BPFP=0.3593 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,112B, BPFP=0.4001 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.72689923 + text_encoder-item0.clip_prompt_embeds 0.00026157 84.56009876 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.76740217 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.09956748 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00189100 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.40456498 4.16816139 + vae.encoder_f1 0.40456539 4.16885519 + vae.decoder 0.00020503 0.02477295 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 4.80007733 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 181128 +BPFP 0.6409 bits/point +EBPFP 1.2818 equivalent bits/point +MSE 4.800077 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.008s, Pack+Encode: 0.289s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8001 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,164B, BPFP=12.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,064B, BPFP=1.0909 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,828B, BPFP=11.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,976B, BPFP=1.0532 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,140B, BPFP=0.9928 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,636B, BPFP=0.5743 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,636B, BPFP=0.5743 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,400B, BPFP=0.8057 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.71026373 + text_encoder-item0.clip_prompt_embeds 0.00027179 23.87456245 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.75278950 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.09373027 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00141798 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00673531 1.57848179 + vae.encoder_f1 0.00673732 1.57706153 + vae.decoder 0.00020129 0.03904563 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 2.01267312 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215700 +BPFP 0.7632 bits/point +EBPFP 1.5264 equivalent bits/point +MSE 2.012673 +---------------------- -------------------------------------------------------- +Time: 0.746s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0127 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,260B, BPFP=13.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,296B, BPFP=1.1223 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,792B, BPFP=11.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,928B, BPFP=1.1305 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,296B, BPFP=1.1489 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,896B, BPFP=0.4714 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,896B, BPFP=0.4714 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,996B, BPFP=0.3966 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.69781597 + text_encoder-item0.clip_prompt_embeds 0.00023057 23.85754236 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.75716338 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.14276830 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00201047 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00881784 1.64466715 + vae.encoder_f1 0.00882136 1.64427292 + vae.decoder 0.00017598 0.02106965 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 2.04329491 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 196216 +BPFP 0.6943 bits/point +EBPFP 1.3885 equivalent bits/point +MSE 2.043295 +---------------------- -------------------------------------------------------- +Time: 0.744s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0433 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,164B, BPFP=12.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,952B, BPFP=1.0758 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,772B, BPFP=1.1179 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,668B, BPFP=1.0823 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,388B, BPFP=0.5247 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,392B, BPFP=0.5248 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 28,364B, BPFP=0.8656 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.70499396 + text_encoder-item0.clip_prompt_embeds 0.00025208 23.86345669 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.75706496 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.12247079 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00168576 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00582247 0.73246998 + vae.encoder_f1 0.00582996 0.73259187 + vae.decoder 0.00016099 0.04057825 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 1.62185547 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215284 +BPFP 0.7617 bits/point +EBPFP 1.5235 equivalent bits/point +MSE 1.621855 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6219 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,220B, BPFP=12.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,780B, BPFP=1.0525 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,764B, BPFP=11.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,760B, BPFP=1.1169 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,040B, BPFP=1.0410 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,168B, BPFP=0.6434 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,172B, BPFP=0.6435 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,532B, BPFP=0.6571 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.73191253 + text_encoder-item0.clip_prompt_embeds 0.00020809 84.58970847 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.75862164 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.10040984 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00142776 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00602745 1.31553161 + vae.encoder_f1 0.00603159 1.31560516 + vae.decoder 0.00017526 0.03380480 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 3.47876521 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 222292 +BPFP 0.7865 bits/point +EBPFP 1.5731 equivalent bits/point +MSE 3.478765 +---------------------- -------------------------------------------------------- +Time: 0.745s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4788 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,540B, BPFP=1.0200 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,772B, BPFP=11.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,384B, BPFP=1.0864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,928B, BPFP=0.9874 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,488B, BPFP=0.6636 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,500B, BPFP=0.6638 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,092B, BPFP=0.7352 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.447s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.69575016 + text_encoder-item0.clip_prompt_embeds 0.00020908 23.85033863 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.75572896 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.08734212 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00134321 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00634616 1.32110512 + vae.encoder_f1 0.00635208 1.32065845 + vae.decoder 0.00022721 0.03507129 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 1.89214926 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224768 +BPFP 0.7953 bits/point +EBPFP 1.5906 equivalent bits/point +MSE 1.892149 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.447s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8921 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,608B, BPFP=1.0292 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,860B, BPFP=11.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,808B, BPFP=1.0396 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,396B, BPFP=0.9993 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,796B, BPFP=0.3173 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,796B, BPFP=0.3173 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,808B, BPFP=0.3298 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.445s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.72107156 + text_encoder-item0.clip_prompt_embeds 0.00022947 47.89095475 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.78897161 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.08515049 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00131983 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.05448642 1.76922607 + vae.encoder_f1 0.05448771 1.76896763 + vae.decoder 0.00017748 0.01948909 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 2.72691881 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 166128 +BPFP 0.5878 bits/point +EBPFP 1.1756 equivalent bits/point +MSE 2.726919 +---------------------- -------------------------------------------------------- +Time: 0.741s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.445s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7269 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,572B, BPFP=1.0244 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,868B, BPFP=11.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,664B, BPFP=1.1091 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,064B, BPFP=1.0416 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,320B, BPFP=0.4474 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,320B, BPFP=0.4474 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,308B, BPFP=0.3451 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.68206366 + text_encoder-item0.clip_prompt_embeds 0.00020169 59.96458587 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.68894200 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.08865010 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00178567 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.06876971 2.37831306 + vae.encoder_f1 0.06877109 2.37915277 + vae.decoder 0.00023999 0.01820294 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 3.32543162 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 186164 +BPFP 0.6587 bits/point +EBPFP 1.3174 equivalent bits/point +MSE 3.325432 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3254 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,916B, BPFP=1.0709 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,720B, BPFP=1.1136 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,684B, BPFP=1.0320 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,540B, BPFP=0.4813 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,540B, BPFP=0.4813 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 29,508B, BPFP=0.9005 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.69910192 + text_encoder-item0.clip_prompt_embeds 0.00025253 34.80743329 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.78663583 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.13926716 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00160659 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00595097 0.72276425 + vae.encoder_f1 0.00595882 0.72268641 + vae.decoder 0.00020134 0.04584344 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 1.90489280 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 208748 +BPFP 0.7386 bits/point +EBPFP 1.4772 equivalent bits/point +MSE 1.904893 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9049 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,224B, BPFP=12.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,012B, BPFP=1.0839 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,040B, BPFP=1.1396 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,144B, BPFP=1.0690 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,096B, BPFP=0.3982 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,096B, BPFP=0.3982 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,720B, BPFP=0.6628 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.69847798 + text_encoder-item0.clip_prompt_embeds 0.00022201 35.94497430 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.73849597 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.10100660 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00158552 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00831743 1.25850034 + vae.encoder_f1 0.00831926 1.25798345 + vae.decoder 0.00028593 0.03220513 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 2.17972107 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 192028 +BPFP 0.6794 bits/point +EBPFP 1.3589 equivalent bits/point +MSE 2.179721 +---------------------- -------------------------------------------------------- +Time: 0.762s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1797 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,372B, BPFP=1.1326 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,792B, BPFP=11.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,136B, BPFP=1.1474 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,388B, BPFP=1.1513 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,508B, BPFP=0.6486 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,508B, BPFP=0.6486 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,272B, BPFP=0.6492 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.70840247 + text_encoder-item0.clip_prompt_embeds 0.00026808 23.88388630 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.78581491 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.15515196 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00202383 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00606586 1.38540554 + vae.encoder_f1 0.00607066 1.38501084 + vae.decoder 0.00019664 0.03288506 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 1.92567796 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 228036 +BPFP 0.8069 bits/point +EBPFP 1.6137 equivalent bits/point +MSE 1.925678 +---------------------- -------------------------------------------------------- +Time: 0.763s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9257 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,128B, BPFP=11.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,280B, BPFP=1.1201 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,792B, BPFP=11.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,056B, BPFP=1.2221 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,644B, BPFP=1.1578 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,644B, BPFP=0.4828 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,648B, BPFP=0.4829 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,056B, BPFP=0.5815 +⌛️ [2/4] FRONTEND: Frontend time: 0.302s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.71900503 + text_encoder-item0.clip_prompt_embeds 0.00023198 23.88785173 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.79386601 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.13971820 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00186311 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.05216765 2.30881977 + vae.encoder_f1 0.05216896 2.30852246 + vae.decoder 0.00017960 0.02927955 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 2.35294927 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 205104 +BPFP 0.7257 bits/point +EBPFP 1.4514 equivalent bits/point +MSE 2.352949 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.009s, Pack+Encode: 0.302s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3529 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,820B, BPFP=1.0579 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,904B, BPFP=11.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,496B, BPFP=1.0955 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,464B, BPFP=1.0264 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,140B, BPFP=0.6583 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,140B, BPFP=0.6583 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,620B, BPFP=0.6598 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.71268980 + text_encoder-item0.clip_prompt_embeds 0.00023125 180.57367762 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.77764106 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.09568045 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00160805 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00620361 1.16681671 + vae.encoder_f1 0.00620966 1.16677511 + vae.decoder 0.00020748 0.03361798 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 5.92002112 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 223624 +BPFP 0.7912 bits/point +EBPFP 1.5825 equivalent bits/point +MSE 5.920021 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.9200 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,176B, BPFP=12.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,016B, BPFP=1.0844 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,700B, BPFP=10.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,824B, BPFP=1.1221 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,240B, BPFP=1.0714 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,680B, BPFP=0.5597 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,676B, BPFP=0.5596 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,768B, BPFP=0.6643 +⌛️ [2/4] FRONTEND: Frontend time: 0.305s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.73864206 + text_encoder-item0.clip_prompt_embeds 0.00023066 47.98833198 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.72112265 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.09738677 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00161054 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.03159856 1.30330145 + vae.encoder_f1 0.03160188 1.30043757 + vae.decoder 0.00018417 0.03157794 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 2.51472367 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 212936 +BPFP 0.7534 bits/point +EBPFP 1.5069 equivalent bits/point +MSE 2.514724 +---------------------- -------------------------------------------------------- +Time: 0.765s Load: 0.008s, Pack+Encode: 0.305s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5147 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,144B, BPFP=11.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,964B, BPFP=1.0774 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,736B, BPFP=10.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,912B, BPFP=1.1292 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,372B, BPFP=1.1001 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 45,072B, BPFP=0.6877 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 45,068B, BPFP=0.6877 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,512B, BPFP=0.6260 +⌛️ [2/4] FRONTEND: Frontend time: 0.304s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.71209590 + text_encoder-item0.clip_prompt_embeds 0.00024948 23.86800130 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.72198110 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.09423403 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00181290 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.03490865 2.38359499 + vae.encoder_f1 0.03491008 2.38435793 + vae.decoder 0.00028462 0.03691420 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 2.38620670 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 229636 +BPFP 0.8125 bits/point +EBPFP 1.6250 equivalent bits/point +MSE 2.386207 +---------------------- -------------------------------------------------------- +Time: 0.762s Load: 0.009s, Pack+Encode: 0.304s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3862 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,220B, BPFP=12.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,560B, BPFP=1.0227 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,792B, BPFP=11.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,652B, BPFP=1.1081 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,196B, BPFP=1.1464 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,496B, BPFP=0.3433 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,496B, BPFP=0.3433 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 28,640B, BPFP=0.8740 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.68594948 + text_encoder-item0.clip_prompt_embeds 0.00021560 23.85373968 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.71019497 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.12774393 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00182982 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00544735 0.57249713 + vae.encoder_f1 0.00544843 0.57249200 + vae.decoder 0.00018632 0.04159172 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 1.54771602 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 193908 +BPFP 0.6861 bits/point +EBPFP 1.3722 equivalent bits/point +MSE 1.547716 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.5477 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,108B, BPFP=11.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,596B, BPFP=1.0276 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,852B, BPFP=11.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,496B, BPFP=1.0955 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,008B, BPFP=1.0402 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,872B, BPFP=0.5931 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,872B, BPFP=0.5931 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,644B, BPFP=0.6605 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.74138069 + text_encoder-item0.clip_prompt_embeds 0.00022698 72.13054654 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.77058358 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.09130928 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00158950 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00630479 1.05291224 + vae.encoder_f1 0.00631430 1.05253959 + vae.decoder 0.00018596 0.03135522 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 3.03035106 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215304 +BPFP 0.7618 bits/point +EBPFP 1.5236 equivalent bits/point +MSE 3.030351 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0304 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,764B, BPFP=1.0503 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,848B, BPFP=11.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,476B, BPFP=1.0938 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,952B, BPFP=0.9880 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,736B, BPFP=0.5605 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,736B, BPFP=0.5605 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,908B, BPFP=0.7601 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.77708848 + text_encoder-item0.clip_prompt_embeds 0.00024643 36.18949963 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.80186033 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.09239052 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00147305 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00612578 0.93698460 + vae.encoder_f1 0.00613243 0.93701804 + vae.decoder 0.00018179 0.03480864 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 2.03710862 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 212460 +BPFP 0.7517 bits/point +EBPFP 1.5035 equivalent bits/point +MSE 2.037109 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0371 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,144B, BPFP=11.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,748B, BPFP=1.1834 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,784B, BPFP=11.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,824B, BPFP=1.3656 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,164B, BPFP=1.2217 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 13,360B, BPFP=0.2039 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 13,360B, BPFP=0.2039 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 28,936B, BPFP=0.8831 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.73536936 + text_encoder-item0.clip_prompt_embeds 0.00024049 23.84416641 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.77079563 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.10545536 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00213609 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00526071 0.26289520 + vae.encoder_f1 0.00526072 0.26288223 + vae.decoder 0.00016981 0.03710318 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 1.40248213 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183176 +BPFP 0.6481 bits/point +EBPFP 1.2963 equivalent bits/point +MSE 1.402482 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.4025 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,124B, BPFP=11.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,760B, BPFP=1.0498 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,324B, BPFP=1.0815 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,616B, BPFP=1.0302 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,152B, BPFP=0.6127 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,152B, BPFP=0.6127 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,080B, BPFP=0.7654 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.67617941 + text_encoder-item0.clip_prompt_embeds 0.00022843 61.13701468 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.74142547 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.13052389 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00154229 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00622977 1.05927539 + vae.encoder_f1 0.00623684 1.05935204 + vae.decoder 0.00019755 0.03483563 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 2.74793935 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 220852 +BPFP 0.7814 bits/point +EBPFP 1.5629 equivalent bits/point +MSE 2.747939 +---------------------- -------------------------------------------------------- +Time: 0.764s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7479 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,932B, BPFP=1.0731 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,916B, BPFP=11.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,616B, BPFP=1.1052 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,304B, BPFP=1.0477 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,620B, BPFP=0.4367 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,620B, BPFP=0.4367 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,668B, BPFP=0.5392 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.73441728 + text_encoder-item0.clip_prompt_embeds 0.00026004 35.94447545 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.79415483 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.09688560 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00155115 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00725303 0.99497223 + vae.encoder_f1 0.00725507 0.99476194 + vae.decoder 0.00017991 0.02650865 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 2.05676201 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 191716 +BPFP 0.6783 bits/point +EBPFP 1.3567 equivalent bits/point +MSE 2.056762 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0568 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,244B, BPFP=12.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,088B, BPFP=1.0942 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,896B, BPFP=11.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,892B, BPFP=1.1276 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,840B, BPFP=1.0613 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,620B, BPFP=0.4367 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,620B, BPFP=0.4367 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,672B, BPFP=0.4172 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.76353057 + text_encoder-item0.clip_prompt_embeds 0.00031748 23.88431962 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.74633398 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.09557084 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00181520 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.42111695 3.64599133 + vae.encoder_f1 0.42111716 3.64620399 + vae.decoder 0.00019827 0.02393389 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 2.97054997 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 188728 +BPFP 0.6678 bits/point +EBPFP 1.3355 equivalent bits/point +MSE 2.970550 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9705 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,068B, BPFP=1.0915 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,748B, BPFP=10.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,380B, BPFP=1.1672 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,704B, BPFP=1.1847 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,588B, BPFP=0.5125 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,588B, BPFP=0.5125 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,088B, BPFP=0.4910 +⌛️ [2/4] FRONTEND: Frontend time: 0.301s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.69031978 + text_encoder-item0.clip_prompt_embeds 0.00024951 72.49118980 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.72678881 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.09890200 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00201321 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.10376993 3.37086391 + vae.encoder_f1 0.10377157 3.36795330 + vae.decoder 0.00019787 0.02658338 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 4.11398190 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 206212 +BPFP 0.7296 bits/point +EBPFP 1.4593 equivalent bits/point +MSE 4.113982 +---------------------- -------------------------------------------------------- +Time: 0.765s Load: 0.009s, Pack+Encode: 0.301s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1140 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,888B, BPFP=1.0671 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,680B, BPFP=10.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,008B, BPFP=1.1370 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,256B, BPFP=1.0465 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,052B, BPFP=0.5349 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,052B, BPFP=0.5349 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,540B, BPFP=0.4742 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.73615424 + text_encoder-item0.clip_prompt_embeds 0.00022350 180.39857278 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.74502149 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.09672606 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00151690 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.01346414 1.71627975 + vae.encoder_f1 0.01346933 1.71588027 + vae.decoder 0.00019243 0.02432662 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 6.16912684 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 202532 +BPFP 0.7166 bits/point +EBPFP 1.4332 equivalent bits/point +MSE 6.169127 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.1691 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,236B, BPFP=12.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,848B, BPFP=1.0617 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,844B, BPFP=11.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,120B, BPFP=1.0649 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,656B, BPFP=1.1327 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,968B, BPFP=0.4573 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,968B, BPFP=0.4573 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,196B, BPFP=0.4637 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.69509570 + text_encoder-item0.clip_prompt_embeds 0.00024958 60.21692370 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.82784443 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.13360034 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00187525 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.11196710 2.76660991 + vae.encoder_f1 0.11196851 2.76797557 + vae.decoder 0.00023459 0.02817797 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 3.51544468 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 194692 +BPFP 0.6889 bits/point +EBPFP 1.3777 equivalent bits/point +MSE 3.515445 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5154 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,236B, BPFP=12.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,504B, BPFP=1.1504 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,860B, BPFP=11.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,092B, BPFP=1.1438 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,912B, BPFP=1.0124 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,660B, BPFP=0.6357 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,660B, BPFP=0.6357 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,996B, BPFP=0.5797 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.72679146 + text_encoder-item0.clip_prompt_embeds 0.00025929 48.55443807 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.73539047 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.11070622 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00152034 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00675017 1.38871253 + vae.encoder_f1 0.00675421 1.38849223 + vae.decoder 0.00023635 0.03425774 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 2.57063684 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 218776 +BPFP 0.7741 bits/point +EBPFP 1.5482 equivalent bits/point +MSE 2.570637 +---------------------- -------------------------------------------------------- +Time: 0.747s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5706 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,220B, BPFP=12.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,784B, BPFP=1.0530 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,324B, BPFP=1.0815 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,884B, BPFP=1.1131 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 48,152B, BPFP=0.7347 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 48,152B, BPFP=0.7347 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,280B, BPFP=0.5884 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.67847212 + text_encoder-item0.clip_prompt_embeds 0.00064775 179.61152259 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.75238972 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.08785686 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00192180 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00728993 1.74825490 + vae.encoder_f1 0.00729572 1.74832201 + vae.decoder 0.00026488 0.03355562 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 6.16420334 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 234448 +BPFP 0.8295 bits/point +EBPFP 1.6591 equivalent bits/point +MSE 6.164203 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.1642 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,220B, BPFP=12.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,984B, BPFP=1.0801 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,472B, BPFP=1.0935 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,516B, BPFP=1.1038 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,868B, BPFP=0.6389 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,868B, BPFP=0.6389 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,592B, BPFP=0.6589 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.69814157 + text_encoder-item0.clip_prompt_embeds 0.00023188 23.85324083 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.72889676 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.09490277 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00185524 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00613207 1.12686515 + vae.encoder_f1 0.00613899 1.12641799 + vae.decoder 0.00023812 0.03377577 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 1.80237917 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224216 +BPFP 0.7933 bits/point +EBPFP 1.5867 equivalent bits/point +MSE 1.802379 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8024 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,228B, BPFP=1.1131 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,780B, BPFP=11.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,832B, BPFP=1.1227 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,944B, BPFP=1.0893 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,120B, BPFP=0.5359 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,120B, BPFP=0.5359 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,228B, BPFP=0.5868 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.70666035 + text_encoder-item0.clip_prompt_embeds 0.00023678 34.88940535 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.75698552 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.09604333 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00155544 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00636537 1.30846965 + vae.encoder_f1 0.00636991 1.30815375 + vae.decoder 0.00025538 0.03293065 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 2.17521038 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 208280 +BPFP 0.7370 bits/point +EBPFP 1.4739 equivalent bits/point +MSE 2.175210 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.457s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1752 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,216B, BPFP=12.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,740B, BPFP=1.0471 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,768B, BPFP=1.0364 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,828B, BPFP=1.0102 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,516B, BPFP=0.5267 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,512B, BPFP=0.5266 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,720B, BPFP=0.4187 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.73610051 + text_encoder-item0.clip_prompt_embeds 0.00023432 48.22164164 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.67672706 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.09113617 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00148144 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.23155926 3.21515059 + vae.encoder_f1 0.23156048 3.21630287 + vae.decoder 0.00018572 0.02406325 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 3.40722407 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 196884 +BPFP 0.6966 bits/point +EBPFP 1.3933 equivalent bits/point +MSE 3.407224 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4072 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,116B, BPFP=11.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,700B, BPFP=1.0417 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,800B, BPFP=11.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,128B, BPFP=1.0656 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,596B, BPFP=1.0551 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,568B, BPFP=0.6648 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,568B, BPFP=0.6648 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,240B, BPFP=0.7092 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.74557511 + text_encoder-item0.clip_prompt_embeds 0.00022528 60.49696885 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.77179151 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.09269043 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00159645 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00729824 1.42478156 + vae.encoder_f1 0.00730369 1.42539001 + vae.decoder 0.00019938 0.03905762 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 2.89972105 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 226572 +BPFP 0.8017 bits/point +EBPFP 1.6033 equivalent bits/point +MSE 2.899721 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8997 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,180B, BPFP=12.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,672B, BPFP=1.0379 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,836B, BPFP=11.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,276B, BPFP=1.0776 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,536B, BPFP=0.9775 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,704B, BPFP=0.4380 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,708B, BPFP=0.4380 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,548B, BPFP=0.8407 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.72806231 + text_encoder-item0.clip_prompt_embeds 0.00022149 84.24970407 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.72660460 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.09624453 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00140672 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00564371 0.84505188 + vae.encoder_f1 0.00565042 0.84504330 + vae.decoder 0.00019980 0.04145462 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 3.25234288 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 198316 +BPFP 0.7017 bits/point +EBPFP 1.4034 equivalent bits/point +MSE 3.252343 +---------------------- -------------------------------------------------------- +Time: 0.763s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2523 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,176B, BPFP=12.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,008B, BPFP=1.0833 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,040B, BPFP=1.1396 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 39,416B, BPFP=0.9998 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,556B, BPFP=0.4815 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,564B, BPFP=0.4816 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,968B, BPFP=0.8230 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.69772037 + text_encoder-item0.clip_prompt_embeds 0.00022173 60.52771577 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.73933887 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.14028011 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00144709 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00576096 0.73990917 + vae.encoder_f1 0.00576981 0.73976880 + vae.decoder 0.00019592 0.03894538 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 2.58473564 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 205408 +BPFP 0.7268 bits/point +EBPFP 1.4536 equivalent bits/point +MSE 2.584736 +---------------------- -------------------------------------------------------- +Time: 0.762s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5847 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,252B, BPFP=13.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,448B, BPFP=1.1429 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,924B, BPFP=1.2114 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,880B, BPFP=1.1638 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,348B, BPFP=0.4020 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,344B, BPFP=0.4020 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,600B, BPFP=0.5371 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.68856637 + text_encoder-item0.clip_prompt_embeds 0.00025917 24.17003813 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.78984218 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.10890496 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00180339 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00594818 0.87327904 + vae.encoder_f1 0.00595328 0.87348956 + vae.decoder 0.00023462 0.03013935 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 1.69342510 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 193428 +BPFP 0.6844 bits/point +EBPFP 1.3688 equivalent bits/point +MSE 1.693425 +---------------------- -------------------------------------------------------- +Time: 0.762s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6934 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,908B, BPFP=1.2051 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,828B, BPFP=11.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,152B, BPFP=1.3110 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,000B, BPFP=1.2175 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,232B, BPFP=0.3698 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,232B, BPFP=0.3698 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,700B, BPFP=0.2960 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.66240128 + text_encoder-item0.clip_prompt_embeds 0.00022579 23.87211682 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.74038830 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.11127388 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00196866 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.85445058 4.42329597 + vae.encoder_f1 0.85445166 4.42328167 + vae.decoder 0.00025257 0.01562365 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 3.33037199 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 185108 +BPFP 0.6550 bits/point +EBPFP 1.3099 equivalent bits/point +MSE 3.330372 +---------------------- -------------------------------------------------------- +Time: 0.764s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3304 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,240B, BPFP=12.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,784B, BPFP=1.0530 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,784B, BPFP=11.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,760B, BPFP=1.1169 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,828B, BPFP=1.0356 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 46,608B, BPFP=0.7112 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 46,596B, BPFP=0.7110 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,964B, BPFP=0.8229 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.73544685 + text_encoder-item0.clip_prompt_embeds 0.00025458 23.85433365 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.77383571 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.09953108 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00161724 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00628510 1.15994954 + vae.encoder_f1 0.00629234 1.15990555 + vae.decoder 0.00023521 0.04239070 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 1.81905023 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 236420 +BPFP 0.8365 bits/point +EBPFP 1.6730 equivalent bits/point +MSE 1.819050 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8191 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,152B, BPFP=12.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,832B, BPFP=1.0595 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,880B, BPFP=11.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,572B, BPFP=1.1016 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,016B, BPFP=1.0911 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,240B, BPFP=0.4919 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,232B, BPFP=0.4918 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,756B, BPFP=0.6945 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.448s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.76238378 + text_encoder-item0.clip_prompt_embeds 0.00022807 108.28132610 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.79043608 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.09323302 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00188857 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00573429 0.83327204 + vae.encoder_f1 0.00574192 0.83366150 + vae.decoder 0.00017875 0.03379140 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 3.87461177 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 205536 +BPFP 0.7272 bits/point +EBPFP 1.4545 equivalent bits/point +MSE 3.874612 +---------------------- -------------------------------------------------------- +Time: 0.748s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.448s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8746 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,812B, BPFP=1.3274 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,820B, BPFP=11.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,340B, BPFP=1.4075 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,112B, BPFP=1.5248 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,192B, BPFP=0.6285 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,192B, BPFP=0.6285 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,816B, BPFP=0.7268 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.67607673 + text_encoder-item0.clip_prompt_embeds 0.00027120 23.83652724 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.74276538 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.11936055 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00277544 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00781570 1.61765790 + vae.encoder_f1 0.00781878 1.61757600 + vae.decoder 0.00029724 0.04120537 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 2.03169703 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 247348 +BPFP 0.8752 bits/point +EBPFP 1.7504 equivalent bits/point +MSE 2.031697 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0317 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,264B, BPFP=13.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,884B, BPFP=1.0666 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,084B, BPFP=1.1432 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,296B, BPFP=1.0728 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,048B, BPFP=0.5806 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,052B, BPFP=0.5806 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 29,876B, BPFP=0.9117 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.69047801 + text_encoder-item0.clip_prompt_embeds 0.00022930 23.88205154 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.68352842 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.09795308 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00179827 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00577752 0.80153757 + vae.encoder_f1 0.00578475 0.80153131 + vae.decoder 0.00024190 0.04130638 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 1.65332824 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224088 +BPFP 0.7929 bits/point +EBPFP 1.5858 equivalent bits/point +MSE 1.653328 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6533 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,180B, BPFP=12.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,688B, BPFP=1.1753 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,912B, BPFP=11.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,020B, BPFP=1.2192 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,944B, BPFP=1.2161 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,284B, BPFP=0.6147 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,288B, BPFP=0.6147 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,160B, BPFP=0.3711 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.78567211 + text_encoder-item0.clip_prompt_embeds 0.00028764 23.89736159 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.77360325 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.09516423 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00215039 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.03343784 2.54749036 + vae.encoder_f1 0.03344063 2.54640889 + vae.decoder 0.00016139 0.01932592 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 2.46065900 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 218332 +BPFP 0.7725 bits/point +EBPFP 1.5450 equivalent bits/point +MSE 2.460659 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4607 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,128B, BPFP=11.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,500B, BPFP=1.0146 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,736B, BPFP=10.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,384B, BPFP=1.0864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,644B, BPFP=1.0563 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 44,976B, BPFP=0.6863 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 44,972B, BPFP=0.6862 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,032B, BPFP=0.7639 +⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.73174024 + text_encoder-item0.clip_prompt_embeds 0.00023094 132.41686113 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.73274784 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.08767836 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00165643 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00637455 1.26454139 + vae.encoder_f1 0.00637988 1.26428425 + vae.decoder 0.00020059 0.03794039 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 4.70589656 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 231228 +BPFP 0.8181 bits/point +EBPFP 1.6363 equivalent bits/point +MSE 4.705897 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7059 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,248B, BPFP=1.1158 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,952B, BPFP=12.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,116B, BPFP=1.1458 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,932B, BPFP=1.1143 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,260B, BPFP=0.5685 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,260B, BPFP=0.5685 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,816B, BPFP=0.7573 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.74109991 + text_encoder-item0.clip_prompt_embeds 0.00025217 23.87234299 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.76715989 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.09479438 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00167286 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00581597 0.81994325 + vae.encoder_f1 0.00582356 0.81996894 + vae.decoder 0.00019494 0.03652266 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 1.66097241 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 219668 +BPFP 0.7772 bits/point +EBPFP 1.5545 equivalent bits/point +MSE 1.660972 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.008s, Pack+Encode: 0.290s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6610 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,180B, BPFP=12.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,504B, BPFP=1.0152 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,872B, BPFP=11.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,688B, BPFP=1.0299 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,700B, BPFP=1.0324 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,740B, BPFP=0.3165 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,744B, BPFP=0.3165 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,560B, BPFP=0.5664 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.75532349 + text_encoder-item0.clip_prompt_embeds 0.00026975 23.85795666 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.74812803 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.08714751 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00152028 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 1.11695218 5.00470114 + vae.encoder_f1 1.11695278 5.00634861 + vae.decoder 0.00019720 0.03146095 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 3.60078205 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 174844 +BPFP 0.6186 bits/point +EBPFP 1.2373 equivalent bits/point +MSE 3.600782 +---------------------- -------------------------------------------------------- +Time: 0.760s Load: 0.007s, Pack+Encode: 0.300s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6008 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,024B, BPFP=1.0855 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,736B, BPFP=10.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,396B, BPFP=1.0873 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,864B, BPFP=1.1126 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,600B, BPFP=0.5585 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,604B, BPFP=0.5585 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,760B, BPFP=0.6641 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.71405554 + text_encoder-item0.clip_prompt_embeds 0.00025545 23.88342549 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.68266177 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.08972834 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00177221 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.01535016 1.85379589 + vae.encoder_f1 0.01535382 1.85346591 + vae.decoder 0.00021460 0.03414334 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 2.14010782 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 214068 +BPFP 0.7574 bits/point +EBPFP 1.5149 equivalent bits/point +MSE 2.140108 +---------------------- -------------------------------------------------------- +Time: 0.760s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1401 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,216B, BPFP=12.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,260B, BPFP=1.1174 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,784B, BPFP=11.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,924B, BPFP=1.2114 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,752B, BPFP=1.1605 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,496B, BPFP=0.4806 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,496B, BPFP=0.4806 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,684B, BPFP=0.9364 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.71056080 + text_encoder-item0.clip_prompt_embeds 0.00022628 23.89241114 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.78993587 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.11115782 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00207668 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00589589 0.74544764 + vae.encoder_f1 0.00590398 0.74539572 + vae.decoder 0.00017838 0.04658822 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 1.62886978 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 216468 +BPFP 0.7659 bits/point +EBPFP 1.5318 equivalent bits/point +MSE 1.628870 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6289 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,832B, BPFP=1.0595 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,748B, BPFP=10.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,472B, BPFP=1.0935 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,504B, BPFP=1.0274 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,824B, BPFP=0.6687 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,828B, BPFP=0.6688 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,944B, BPFP=0.4866 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.69986876 + text_encoder-item0.clip_prompt_embeds 0.00031548 23.86616866 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.78187857 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.09603573 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00138437 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00725484 1.74048376 + vae.encoder_f1 0.00725992 1.74148130 + vae.decoder 0.00019960 0.02803785 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 2.08697807 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 219236 +BPFP 0.7757 bits/point +EBPFP 1.5514 equivalent bits/point +MSE 2.086978 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0870 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,224B, BPFP=12.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,256B, BPFP=0.9816 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,992B, BPFP=12.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 12,292B, BPFP=0.9977 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 38,152B, BPFP=0.9677 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,584B, BPFP=0.5735 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,584B, BPFP=0.5735 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,912B, BPFP=0.4856 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.71291089 + text_encoder-item0.clip_prompt_embeds 0.00021831 72.48449337 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 0.83105364 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.08552236 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00130960 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00923516 1.62046814 + vae.encoder_f1 0.00923823 1.62032187 + vae.decoder 0.00019521 0.02364207 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 3.30171430 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 202852 +BPFP 0.7177 bits/point +EBPFP 1.4355 equivalent bits/point +MSE 3.301714 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3017 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,148B, BPFP=11.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,752B, BPFP=1.0487 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,700B, BPFP=1.1120 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,392B, BPFP=1.1006 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,188B, BPFP=0.5980 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,188B, BPFP=0.5980 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,556B, BPFP=0.5663 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.73983606 + text_encoder-item0.clip_prompt_embeds 0.00062166 204.65912473 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.73271084 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.09621968 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00182087 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00831779 1.58473682 + vae.encoder_f1 0.00832197 1.58473492 + vae.decoder 0.00023271 0.03082696 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 6.74351398 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215576 +BPFP 0.7628 bits/point +EBPFP 1.5255 equivalent bits/point +MSE 6.743514 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.7435 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,984B, BPFP=1.0801 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,748B, BPFP=10.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,172B, BPFP=1.0692 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,412B, BPFP=1.0504 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,708B, BPFP=0.4991 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,712B, BPFP=0.4991 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,772B, BPFP=0.5729 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.72518921 + text_encoder-item0.clip_prompt_embeds 0.00022938 216.64864042 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.76001267 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.08965875 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00165736 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00626977 1.09375298 + vae.encoder_f1 0.00627489 1.09387529 + vae.decoder 0.00017842 0.03312215 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 6.82939240 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 200592 +BPFP 0.7097 bits/point +EBPFP 1.4195 equivalent bits/point +MSE 6.829392 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.8294 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,028B, BPFP=1.0860 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,732B, BPFP=10.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,056B, BPFP=1.1409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,988B, BPFP=1.0904 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,396B, BPFP=0.5706 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,396B, BPFP=0.5706 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 29,224B, BPFP=0.8918 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.67925747 + text_encoder-item0.clip_prompt_embeds 0.00022180 23.87447790 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.74525175 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.09460241 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00161689 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00585720 0.82814848 + vae.encoder_f1 0.00586586 0.82798344 + vae.decoder 0.00016520 0.04010475 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 1.66515507 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 222880 +BPFP 0.7886 bits/point +EBPFP 1.5772 equivalent bits/point +MSE 1.665155 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6652 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,676B, BPFP=1.0384 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,640B, BPFP=10.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,572B, BPFP=1.1016 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,464B, BPFP=1.0771 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,736B, BPFP=0.4080 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,736B, BPFP=0.4080 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,836B, BPFP=0.4528 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.74981562 + text_encoder-item0.clip_prompt_embeds 0.00025784 23.89007119 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.64587998 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.09204502 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00185181 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00734802 1.36481094 + vae.encoder_f1 0.00734987 1.36534286 + vae.decoder 0.00018093 0.02150980 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 1.91234453 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 185688 +BPFP 0.6570 bits/point +EBPFP 1.3140 equivalent bits/point +MSE 1.912345 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9123 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,292B, BPFP=1.1218 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,948B, BPFP=1.1321 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,920B, BPFP=1.0633 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,140B, BPFP=0.6583 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,140B, BPFP=0.6583 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,668B, BPFP=0.6918 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.72767917 + text_encoder-item0.clip_prompt_embeds 0.00023510 96.53285647 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.71717668 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.09376053 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00168749 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00637359 1.39652562 + vae.encoder_f1 0.00637830 1.39656472 + vae.decoder 0.00018566 0.03650965 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 3.82872648 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 226896 +BPFP 0.8028 bits/point +EBPFP 1.6056 equivalent bits/point +MSE 3.828726 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8287 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,216B, BPFP=12.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,736B, BPFP=1.0465 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,836B, BPFP=11.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,844B, BPFP=1.1237 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,096B, BPFP=1.0931 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,348B, BPFP=0.4783 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,344B, BPFP=0.4783 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,264B, BPFP=0.4353 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.68890643 + text_encoder-item0.clip_prompt_embeds 0.00026418 168.91162744 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.73830991 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.10402882 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00175933 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.01530954 1.85055256 + vae.encoder_f1 0.01531230 1.85108721 + vae.decoder 0.00017892 0.02338526 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 5.93139825 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 195540 +BPFP 0.6919 bits/point +EBPFP 1.3837 equivalent bits/point +MSE 5.931398 +---------------------- -------------------------------------------------------- +Time: 0.750s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.9314 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,112B, BPFP=1.0974 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,748B, BPFP=10.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,416B, BPFP=1.1701 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,224B, BPFP=1.1218 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,512B, BPFP=0.6334 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,512B, BPFP=0.6334 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,608B, BPFP=0.6899 +⌛️ [2/4] FRONTEND: Frontend time: 0.356s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.65173705 + text_encoder-item0.clip_prompt_embeds 0.00021481 23.84033837 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.69352546 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.09776145 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00179796 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00591154 1.20416701 + vae.encoder_f1 0.00591973 1.20380056 + vae.decoder 0.00025286 0.03621306 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 1.83827398 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 226216 +BPFP 0.8004 bits/point +EBPFP 1.6008 equivalent bits/point +MSE 1.838274 +---------------------- -------------------------------------------------------- +Time: 0.822s Load: 0.009s, Pack+Encode: 0.356s, Decode+Unpack: 0.457s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8383 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,160B, BPFP=12.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,328B, BPFP=1.1266 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,696B, BPFP=10.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,884B, BPFP=1.2081 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,724B, BPFP=1.1852 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,236B, BPFP=0.4156 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,236B, BPFP=0.4156 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,748B, BPFP=0.7552 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.72667805 + text_encoder-item0.clip_prompt_embeds 0.00023458 47.94524909 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.76934438 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.10960760 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00207548 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00588703 0.67227960 + vae.encoder_f1 0.00589573 0.67207694 + vae.decoder 0.00053402 0.03932668 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 2.22308534 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 202868 +BPFP 0.7178 bits/point +EBPFP 1.4356 equivalent bits/point +MSE 2.223085 +---------------------- -------------------------------------------------------- +Time: 0.762s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.457s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2231 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,152B, BPFP=12.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,124B, BPFP=1.0990 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,744B, BPFP=10.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,488B, BPFP=1.1760 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 44,560B, BPFP=1.1303 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,824B, BPFP=0.5771 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,824B, BPFP=0.5771 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,432B, BPFP=0.4709 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.70800924 + text_encoder-item0.clip_prompt_embeds 0.00022882 179.77455357 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.78738065 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.09597274 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00184666 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00659691 1.45184290 + vae.encoder_f1 0.00660300 1.45062220 + vae.decoder 0.00023739 0.02800165 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 6.03043155 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 212004 +BPFP 0.7501 bits/point +EBPFP 1.5003 equivalent bits/point +MSE 6.030432 +---------------------- -------------------------------------------------------- +Time: 0.761s Load: 0.010s, Pack+Encode: 0.296s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.0304 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,040B, BPFP=1.0877 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,788B, BPFP=1.1192 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,520B, BPFP=1.0532 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,748B, BPFP=0.4844 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,748B, BPFP=0.4844 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 29,888B, BPFP=0.9121 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.72878051 + text_encoder-item0.clip_prompt_embeds 0.00023928 23.88560691 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.77135100 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.09169047 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00161465 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00583864 0.62994695 + vae.encoder_f1 0.00583800 0.62994987 + vae.decoder 0.00018889 0.04093897 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 1.57356662 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210576 +BPFP 0.7451 bits/point +EBPFP 1.4901 equivalent bits/point +MSE 1.573567 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.5736 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,212B, BPFP=12.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,232B, BPFP=1.1136 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,624B, BPFP=10.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,352B, BPFP=1.1649 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,432B, BPFP=1.1778 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,260B, BPFP=0.3854 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,256B, BPFP=0.3854 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,248B, BPFP=0.7400 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.72019958 + text_encoder-item0.clip_prompt_embeds 0.00024821 35.93537355 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.63286347 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.09552256 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00244575 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00570467 0.56565225 + vae.encoder_f1 0.00570488 0.56567067 + vae.decoder 0.00017302 0.03371335 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 1.85827663 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 197472 +BPFP 0.6987 bits/point +EBPFP 1.3974 equivalent bits/point +MSE 1.858277 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8583 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,168B, BPFP=1.1050 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,784B, BPFP=11.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,052B, BPFP=1.1406 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,560B, BPFP=1.0795 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,200B, BPFP=0.3540 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,204B, BPFP=0.3541 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,420B, BPFP=0.4095 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.71718129 + text_encoder-item0.clip_prompt_embeds 0.00021458 143.94634402 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.67427039 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.09815901 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00166748 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00914783 1.38587022 + vae.encoder_f1 0.00914958 1.38636267 + vae.decoder 0.00017527 0.02251934 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 5.06252360 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 178448 +BPFP 0.6314 bits/point +EBPFP 1.2628 equivalent bits/point +MSE 5.062524 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.007s, Pack+Encode: 0.296s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.0625 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,188B, BPFP=12.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,584B, BPFP=1.1613 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,784B, BPFP=11.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,304B, BPFP=1.2422 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,588B, BPFP=1.2071 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,980B, BPFP=0.4422 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,984B, BPFP=0.4423 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,172B, BPFP=0.9208 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.72431517 + text_encoder-item0.clip_prompt_embeds 0.00022150 23.89789426 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.78494349 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.11446821 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00203174 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00578482 0.68730742 + vae.encoder_f1 0.00579739 0.68736565 + vae.decoder 0.00017668 0.04222021 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 1.60170860 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 213440 +BPFP 0.7552 bits/point +EBPFP 1.5104 equivalent bits/point +MSE 1.601709 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.6017 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,124B, BPFP=11.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,880B, BPFP=1.0660 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,768B, BPFP=11.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,512B, BPFP=1.0968 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,508B, BPFP=1.0275 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,864B, BPFP=0.4404 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,864B, BPFP=0.4404 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,240B, BPFP=0.6482 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.450s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.75690571 + text_encoder-item0.clip_prompt_embeds 0.00023894 23.89005005 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.75086336 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.08992824 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00163999 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00958025 1.49165285 + vae.encoder_f1 0.00958229 1.49215984 + vae.decoder 0.00019995 0.03442725 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 1.97260112 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 194616 +BPFP 0.6886 bits/point +EBPFP 1.3772 equivalent bits/point +MSE 1.972601 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9726 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,176B, BPFP=12.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,632B, BPFP=1.1677 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,884B, BPFP=1.2893 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,460B, BPFP=1.2038 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,764B, BPFP=0.3626 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,768B, BPFP=0.3627 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,604B, BPFP=0.9340 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.449s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.70422324 + text_encoder-item0.clip_prompt_embeds 0.00023387 144.85617898 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.76169186 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.11794417 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00197572 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00567713 0.61385292 + vae.encoder_f1 0.00567905 0.61380500 + vae.decoder 0.00019376 0.04780801 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 4.73204050 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 203940 +BPFP 0.7216 bits/point +EBPFP 1.4432 equivalent bits/point +MSE 4.732040 +---------------------- -------------------------------------------------------- +Time: 0.749s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.449s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7320 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,176B, BPFP=12.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,112B, BPFP=1.0974 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,780B, BPFP=1.1185 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,092B, BPFP=1.1438 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,652B, BPFP=0.4372 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,644B, BPFP=0.4371 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,080B, BPFP=0.5518 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.69180346 + text_encoder-item0.clip_prompt_embeds 0.00024281 23.89556700 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.72633390 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.09243475 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00198832 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.02387581 1.55406916 + vae.encoder_f1 0.02387858 1.55429351 + vae.decoder 0.00018648 0.02836093 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 2.00104509 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 196216 +BPFP 0.6943 bits/point +EBPFP 1.3885 equivalent bits/point +MSE 2.001045 +---------------------- -------------------------------------------------------- +Time: 0.754s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.0010 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,256B, BPFP=13.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,920B, BPFP=1.0714 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,652B, BPFP=1.1081 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,636B, BPFP=1.0815 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 44,116B, BPFP=0.6732 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 44,120B, BPFP=0.6732 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,984B, BPFP=0.4268 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.68528517 + text_encoder-item0.clip_prompt_embeds 0.00022399 36.46524959 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.71722651 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.09650451 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00169079 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.01169517 2.12410545 + vae.encoder_f1 0.01169969 2.12319183 + vae.decoder 0.00021186 0.02316622 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 2.59343080 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 220268 +BPFP 0.7794 bits/point +EBPFP 1.5587 equivalent bits/point +MSE 2.593431 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5934 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,164B, BPFP=1.1044 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,784B, BPFP=11.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,672B, BPFP=1.1097 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,012B, BPFP=1.1925 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,660B, BPFP=0.5289 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,660B, BPFP=0.5289 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,456B, BPFP=0.7158 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.67753641 + text_encoder-item0.clip_prompt_embeds 0.00022123 178.37631899 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.71572723 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.09479622 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00200600 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.32749966 4.76376152 + vae.encoder_f1 0.32750070 4.76381779 + vae.decoder 0.00039956 0.03718672 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 7.53110420 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215456 +BPFP 0.7623 bits/point +EBPFP 1.5247 equivalent bits/point +MSE 7.531104 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.5311 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,892B, BPFP=1.0676 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,712B, BPFP=10.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,264B, BPFP=1.1578 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,392B, BPFP=1.0246 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,796B, BPFP=0.4547 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,792B, BPFP=0.4546 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,228B, BPFP=0.7089 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.68737523 + text_encoder-item0.clip_prompt_embeds 0.00024675 36.22805482 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.71730471 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.09905489 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00171478 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00566967 0.77119124 + vae.encoder_f1 0.00567867 0.77115101 + vae.decoder 0.00017839 0.03530842 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 1.96151411 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 199136 +BPFP 0.7046 bits/point +EBPFP 1.4092 equivalent bits/point +MSE 1.961514 +---------------------- -------------------------------------------------------- +Time: 0.751s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.451s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.9615 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,216B, BPFP=12.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,384B, BPFP=0.9989 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,880B, BPFP=11.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,052B, BPFP=1.0594 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,040B, BPFP=1.0664 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,628B, BPFP=0.4521 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,628B, BPFP=0.4521 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,308B, BPFP=1.0165 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.68476470 + text_encoder-item0.clip_prompt_embeds 0.00022364 36.19345661 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.80154657 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.08481516 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00169370 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00580750 0.62134331 + vae.encoder_f1 0.00580664 0.62133121 + vae.decoder 0.00018044 0.04518942 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 1.89168978 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 208992 +BPFP 0.7395 bits/point +EBPFP 1.4789 equivalent bits/point +MSE 1.891690 +---------------------- -------------------------------------------------------- +Time: 0.752s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 1.8917 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,176B, BPFP=12.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,688B, BPFP=1.0400 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,716B, BPFP=10.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,480B, BPFP=1.0942 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 40,124B, BPFP=1.0178 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,704B, BPFP=0.4838 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,704B, BPFP=0.4838 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,112B, BPFP=0.5222 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.70290701 + text_encoder-item0.clip_prompt_embeds 0.00030118 180.01420455 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.69721303 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.10285645 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00166335 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.03869025 2.25920892 + vae.encoder_f1 0.03869358 2.25937891 + vae.decoder 0.00021614 0.02936267 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 6.41183221 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 195560 +BPFP 0.6919 bits/point +EBPFP 1.3839 equivalent bits/point +MSE 6.411832 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.4118 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,136B, BPFP=11.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,660B, BPFP=1.0363 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,256B, BPFP=1.0760 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 41,232B, BPFP=1.0459 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,208B, BPFP=0.6593 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,212B, BPFP=0.6594 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,984B, BPFP=0.4878 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.72833323 + text_encoder-item0.clip_prompt_embeds 0.00023260 48.31606805 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.75594940 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.09011143 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00155350 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00839879 1.99729729 + vae.encoder_f1 0.00840224 1.99744511 + vae.decoder 0.00019463 0.02811776 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 2.84513701 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 218384 +BPFP 0.7727 bits/point +EBPFP 1.5454 equivalent bits/point +MSE 2.845137 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8451 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,552B, BPFP=1.1569 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,820B, BPFP=11.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,580B, BPFP=1.2646 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,540B, BPFP=1.1805 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,624B, BPFP=0.6199 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,624B, BPFP=0.6199 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,324B, BPFP=0.4677 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.452s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.70346236 + text_encoder-item0.clip_prompt_embeds 0.00023544 23.88248487 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.73937860 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.10965631 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00212174 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.01160815 1.77990377 + vae.encoder_f1 0.01161249 1.77973258 + vae.decoder 0.00021720 0.02716463 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 2.10598806 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 221128 +BPFP 0.7824 bits/point +EBPFP 1.5648 equivalent bits/point +MSE 2.105988 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.010s, Pack+Encode: 0.296s, Decode+Unpack: 0.452s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1060 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,212B, BPFP=12.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,112B, BPFP=1.0974 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,932B, BPFP=1.1308 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 42,144B, BPFP=1.0690 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,556B, BPFP=0.3900 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,556B, BPFP=0.3900 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,108B, BPFP=0.4305 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.70934558 + text_encoder-item0.clip_prompt_embeds 0.00022923 24.41339243 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.74134874 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.09691072 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00176688 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.02989292 2.01077747 + vae.encoder_f1 0.02989391 2.00916672 + vae.decoder 0.00034944 0.02722410 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 2.22601685 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 183236 +BPFP 0.6483 bits/point +EBPFP 1.2967 equivalent bits/point +MSE 2.226017 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.2260 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,236B, BPFP=1.1142 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,028B, BPFP=1.1386 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 43,296B, BPFP=1.0982 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,512B, BPFP=15.7500 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 5,840B, BPFP=0.7900 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,440B, BPFP=15.2500 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 11,196B, BPFP=0.9088 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 29,868B, BPFP=0.7576 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,160B, BPFP=0.6281 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,164B, BPFP=0.6281 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,280B, BPFP=0.6494 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.76701474 + text_encoder-item0.clip_prompt_embeds 0.00024627 108.54223316 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.78899741 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.09583166 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00163961 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.73627949 + text_encoder-item3.clip_prompt_embeds 0.00023247 23.82810175 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.45692472 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.52938431 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00165899 + vae.encoder_f0 0.00613025 0.99453282 + vae.encoder_f1 0.00613536 0.99439836 + vae.decoder 0.00018697 0.03398916 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 3.95620412 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 223060 +BPFP 0.7892 bits/point +EBPFP 1.5785 equivalent bits/point +MSE 3.956204 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9562 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.01/hyperprior-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.7341 bits/point +Avg EBPFP 1.4682 equivalent bits/point +Avg MSE 3.037237 +Avg Time 0.758s +------------------------ ---------------------------- diff --git a/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log b/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..ba262f1c8b22ea7b2c66eae18356fb59ac0d059f --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log @@ -0,0 +1,21862 @@ +Experiment: dtufc_elic-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: sd35 + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 520 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,364B, BPFP=24.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,844B, BPFP=1.7376 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,388B, BPFP=21.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,276B, BPFP=1.8081 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 72,472B, BPFP=1.8383 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 59,204B, BPFP=0.9034 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 59,200B, BPFP=0.9033 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,328B, BPFP=1.0171 +⌛️ [2/4] FRONTEND: Frontend time: 3.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.657s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.16037265 + text_encoder-item0.clip_prompt_embeds 0.00025464 43.41924547 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.17734075 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.05921118 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00075367 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00635250 0.59309518 + vae.encoder_f1 0.00635834 0.59359038 + vae.decoder 0.00019940 0.01847607 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 2.92178481 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 339032 +BPFP 1.1996 bits/point +EBPFP 2.3992 equivalent bits/point +MSE 2.921785 +---------------------- -------------------------------------------------------- +Time: 4.805s Load: 0.007s, Pack+Encode: 3.141s, Decode+Unpack: 1.657s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9218 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,256B, BPFP=23.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,464B, BPFP=1.6861 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,516B, BPFP=21.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,344B, BPFP=1.8136 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,576B, BPFP=1.7648 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,764B, BPFP=0.6678 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,800B, BPFP=0.6683 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 29,720B, BPFP=0.9070 +⌛️ [2/4] FRONTEND: Frontend time: 2.165s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.15400416 + text_encoder-item0.clip_prompt_embeds 0.00022609 52.99325284 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.17610109 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.05329766 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00085085 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.01130640 0.70730984 + vae.encoder_f1 0.01130902 0.70525455 + vae.decoder 0.00020860 0.01727775 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 3.22418361 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 301396 +BPFP 1.0664 bits/point +EBPFP 2.1328 equivalent bits/point +MSE 3.224184 +---------------------- -------------------------------------------------------- +Time: 3.772s Load: 0.008s, Pack+Encode: 2.165s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2242 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,284B, BPFP=23.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,228B, BPFP=1.5189 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,756B, BPFP=23.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,220B, BPFP=1.5601 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,888B, BPFP=1.5444 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,216B, BPFP=0.3542 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,236B, BPFP=0.3546 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,988B, BPFP=0.7015 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.15975692 + text_encoder-item0.clip_prompt_embeds 0.00022402 140.66949067 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 0.17505083 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.05983251 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00069944 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 1.19630027 3.07686949 + vae.encoder_f1 1.19630098 3.07435679 + vae.decoder 0.00023596 0.01691304 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 6.61638904 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 240772 +BPFP 0.8519 bits/point +EBPFP 1.7038 equivalent bits/point +MSE 6.616389 +---------------------- -------------------------------------------------------- +Time: 3.764s Load: 0.007s, Pack+Encode: 2.152s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.6164 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,288B, BPFP=23.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,040B, BPFP=1.6288 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,360B, BPFP=21.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,036B, BPFP=1.7886 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,112B, BPFP=1.7277 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 55,032B, BPFP=0.8397 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 55,036B, BPFP=0.8398 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 47,476B, BPFP=1.4489 +⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.619s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.15343612 + text_encoder-item0.clip_prompt_embeds 0.00030342 42.85457251 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.19479941 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.05823447 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00064093 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00586287 0.52313739 + vae.encoder_f1 0.00587438 0.52311277 + vae.decoder 0.00017677 0.02199787 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 2.87480867 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 339336 +BPFP 1.2007 bits/point +EBPFP 2.4013 equivalent bits/point +MSE 2.874809 +---------------------- -------------------------------------------------------- +Time: 3.782s Load: 0.009s, Pack+Encode: 2.155s, Decode+Unpack: 1.619s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8748 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,240B, BPFP=23.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,048B, BPFP=1.4946 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,464B, BPFP=21.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,940B, BPFP=1.5373 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,432B, BPFP=1.5582 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,280B, BPFP=0.5994 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,284B, BPFP=0.5994 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,268B, BPFP=0.8016 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.15236074 + text_encoder-item0.clip_prompt_embeds 0.00024120 286.87635281 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.19477203 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.05964621 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00067582 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00779453 0.57148510 + vae.encoder_f1 0.00779802 0.57272929 + vae.decoder 0.00023829 0.01764486 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 9.27944948 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 275912 +BPFP 0.9763 bits/point +EBPFP 1.9525 equivalent bits/point +MSE 9.279449 +---------------------- -------------------------------------------------------- +Time: 3.762s Load: 0.007s, Pack+Encode: 2.153s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.2794 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,236B, BPFP=23.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,952B, BPFP=1.6169 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,456B, BPFP=21.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,484B, BPFP=1.6627 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,820B, BPFP=1.6188 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 57,440B, BPFP=0.8765 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 57,444B, BPFP=0.8765 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 31,316B, BPFP=0.9557 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.17159563 + text_encoder-item0.clip_prompt_embeds 0.00025651 35.10472682 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.18491679 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.05226172 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00064153 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00655775 0.61628240 + vae.encoder_f1 0.00656268 0.61689174 + vae.decoder 0.00020283 0.01755184 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 2.71468180 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 322104 +BPFP 1.1397 bits/point +EBPFP 2.2794 equivalent bits/point +MSE 2.714682 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.610s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7147 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,284B, BPFP=23.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,868B, BPFP=1.4702 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,468B, BPFP=21.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,096B, BPFP=1.4688 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 57,536B, BPFP=1.4594 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 57,716B, BPFP=0.8807 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 57,712B, BPFP=0.8806 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 41,624B, BPFP=1.2703 +⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.17383804 + text_encoder-item0.clip_prompt_embeds 0.00022242 23.50298887 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.19261245 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.05103497 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00062858 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00593415 0.55535716 + vae.encoder_f1 0.00594307 0.55557418 + vae.decoder 0.00018992 0.02106139 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 2.38325019 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 323260 +BPFP 1.1438 bits/point +EBPFP 2.2876 equivalent bits/point +MSE 2.383250 +---------------------- -------------------------------------------------------- +Time: 3.767s Load: 0.009s, Pack+Encode: 2.155s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3833 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,232B, BPFP=23.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,964B, BPFP=1.6185 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,324B, BPFP=20.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,548B, BPFP=1.7490 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 65,112B, BPFP=1.6516 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 47,896B, BPFP=0.7308 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 47,864B, BPFP=0.7303 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,656B, BPFP=0.8440 +⌛️ [2/4] FRONTEND: Frontend time: 2.164s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.16024538 + text_encoder-item0.clip_prompt_embeds 0.00022110 75.55318249 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.20867701 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.04644808 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00087576 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00641770 0.54974091 + vae.encoder_f1 0.00642053 0.55022442 + vae.decoder 0.00017498 0.01509108 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 3.74122130 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 301552 +BPFP 1.0670 bits/point +EBPFP 2.1339 equivalent bits/point +MSE 3.741221 +---------------------- -------------------------------------------------------- +Time: 3.777s Load: 0.008s, Pack+Encode: 2.164s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7412 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,320B, BPFP=24.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,492B, BPFP=1.4194 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,508B, BPFP=21.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,988B, BPFP=1.5412 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 64,960B, BPFP=1.6477 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 44,144B, BPFP=0.6736 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 44,120B, BPFP=0.6732 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 39,068B, BPFP=1.1923 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.609s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.14772820 + text_encoder-item0.clip_prompt_embeds 0.00021654 317.93442235 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.17563323 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.05902204 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00071474 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00577698 0.49973232 + vae.encoder_f1 0.00578348 0.50064027 + vae.decoder 0.00017559 0.01750978 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 10.05836544 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 301556 +BPFP 1.0670 bits/point +EBPFP 2.1340 equivalent bits/point +MSE 10.058365 +---------------------- -------------------------------------------------------- +Time: 3.762s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.609s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 10.0584 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,236B, BPFP=23.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,260B, BPFP=1.5233 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,328B, BPFP=20.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,180B, BPFP=1.6380 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,568B, BPFP=1.7392 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 49,536B, BPFP=0.7559 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 49,564B, BPFP=0.7563 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,064B, BPFP=0.9175 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.19917794 + text_encoder-item0.clip_prompt_embeds 0.00022160 77.91839996 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.17277144 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.04190491 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00067714 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00668450 0.54482794 + vae.encoder_f1 0.00668875 0.54330224 + vae.decoder 0.00023059 0.01879731 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 3.80053578 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 308692 +BPFP 1.0922 bits/point +EBPFP 2.1845 equivalent bits/point +MSE 3.800536 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8005 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,376B, BPFP=24.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,328B, BPFP=1.6677 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,336B, BPFP=20.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,076B, BPFP=1.7107 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,320B, BPFP=1.7330 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,436B, BPFP=0.6323 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,428B, BPFP=0.6321 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,352B, BPFP=0.6211 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.14511351 + text_encoder-item0.clip_prompt_embeds 0.00023190 89.52374188 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.16775749 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.05306912 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00066556 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.04018118 0.81567943 + vae.encoder_f1 0.04018488 0.81805491 + vae.decoder 0.00016201 0.01354473 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 4.23044400 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 284608 +BPFP 1.0070 bits/point +EBPFP 2.0140 equivalent bits/point +MSE 4.230444 +---------------------- -------------------------------------------------------- +Time: 3.760s Load: 0.009s, Pack+Encode: 2.139s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2304 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,328B, BPFP=24.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,176B, BPFP=1.5119 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,452B, BPFP=21.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,944B, BPFP=1.5377 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,712B, BPFP=1.6161 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 50,084B, BPFP=0.7642 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 50,080B, BPFP=0.7642 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 28,460B, BPFP=0.8685 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.17868036 + text_encoder-item0.clip_prompt_embeds 0.00023140 141.70440172 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.17372320 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.04331999 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00078179 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.04874706 0.84256560 + vae.encoder_f1 0.04875064 0.83943236 + vae.decoder 0.00019641 0.01497553 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 5.60618695 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 302192 +BPFP 1.0692 bits/point +EBPFP 2.1385 equivalent bits/point +MSE 5.606187 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6062 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,344B, BPFP=24.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,472B, BPFP=1.5519 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,312B, BPFP=20.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,532B, BPFP=1.5854 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,720B, BPFP=1.5402 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 60,684B, BPFP=0.9260 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 60,640B, BPFP=0.9253 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,144B, BPFP=0.7063 +⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.13324536 + text_encoder-item0.clip_prompt_embeds 0.00030893 53.85283922 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.18627477 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.05246485 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00064552 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.01360236 0.79972041 + vae.encoder_f1 0.01360807 0.80173808 + vae.decoder 0.00023006 0.01714638 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 3.29038606 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 315804 +BPFP 1.1174 bits/point +EBPFP 2.2348 equivalent bits/point +MSE 3.290386 +---------------------- -------------------------------------------------------- +Time: 3.770s Load: 0.008s, Pack+Encode: 2.162s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2904 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,248B, BPFP=23.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,396B, BPFP=1.6769 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,376B, BPFP=21.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,388B, BPFP=1.7360 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,516B, BPFP=1.7887 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,428B, BPFP=0.3270 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,428B, BPFP=0.3270 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,072B, BPFP=0.3379 +⌛️ [2/4] FRONTEND: Frontend time: 2.131s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.17260293 + text_encoder-item0.clip_prompt_embeds 0.00024198 63.81886668 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.19646239 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.07884572 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00068150 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 1.67190456 3.40906739 + vae.encoder_f1 1.67190480 3.41801357 + vae.decoder 0.00017417 0.00832015 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 4.76293577 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 237808 +BPFP 0.8414 bits/point +EBPFP 1.6829 equivalent bits/point +MSE 4.762936 +---------------------- -------------------------------------------------------- +Time: 3.731s Load: 0.007s, Pack+Encode: 2.131s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7629 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,252B, BPFP=23.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,380B, BPFP=1.5395 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,284B, BPFP=20.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,868B, BPFP=1.6127 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,648B, BPFP=1.7920 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 63,296B, BPFP=0.9658 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 63,300B, BPFP=0.9659 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 35,572B, BPFP=1.0856 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.13460732 + text_encoder-item0.clip_prompt_embeds 0.00025129 53.17272896 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.21362059 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.04223134 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00083364 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00621760 0.60191727 + vae.encoder_f1 0.00622505 0.60018814 + vae.decoder 0.00025114 0.02049554 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 3.17997863 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 343556 +BPFP 1.2156 bits/point +EBPFP 2.4312 equivalent bits/point +MSE 3.179979 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1800 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,204B, BPFP=22.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,688B, BPFP=1.4459 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,468B, BPFP=21.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,168B, BPFP=1.5558 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,904B, BPFP=1.5448 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 65,572B, BPFP=1.0005 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 65,580B, BPFP=1.0007 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 36,180B, BPFP=1.1041 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.21120721 + text_encoder-item0.clip_prompt_embeds 0.00020838 43.98228659 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.18491266 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.05603871 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00067333 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00675961 0.68917894 + vae.encoder_f1 0.00676652 0.68955696 + vae.decoder 0.00021373 0.02084894 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 2.98119167 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 337720 +BPFP 1.1949 bits/point +EBPFP 2.3899 equivalent bits/point +MSE 2.981192 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9812 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,232B, BPFP=23.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,268B, BPFP=1.5244 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,300B, BPFP=20.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,400B, BPFP=1.6558 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,704B, BPFP=1.8188 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,036B, BPFP=0.5956 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,036B, BPFP=0.5956 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 54,544B, BPFP=1.6646 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.14650366 + text_encoder-item0.clip_prompt_embeds 0.00021387 90.42366748 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.17987248 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.05365184 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00067569 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00596338 0.43080303 + vae.encoder_f1 0.00596322 0.43075785 + vae.decoder 0.00018207 0.02345705 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 4.07611014 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 315476 +BPFP 1.1162 bits/point +EBPFP 2.2325 equivalent bits/point +MSE 4.076110 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0761 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,248B, BPFP=23.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,424B, BPFP=1.5455 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,400B, BPFP=21.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,448B, BPFP=1.5786 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,860B, BPFP=1.5437 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,572B, BPFP=0.4360 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,572B, BPFP=0.4360 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 44,296B, BPFP=1.3518 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.12811538 + text_encoder-item0.clip_prompt_embeds 0.00022138 203.29822781 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.17463790 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.05530069 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00067538 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00552804 0.35862291 + vae.encoder_f1 0.00552758 0.35856766 + vae.decoder 0.00018040 0.02047906 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 6.99457217 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 272776 +BPFP 0.9652 bits/point +EBPFP 1.9303 equivalent bits/point +MSE 6.994572 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.9946 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,364B, BPFP=24.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,368B, BPFP=1.5379 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,528B, BPFP=22.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,204B, BPFP=1.5588 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,536B, BPFP=1.5609 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,212B, BPFP=0.4915 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,180B, BPFP=0.4910 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,224B, BPFP=0.7698 +⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.15973969 + text_encoder-item0.clip_prompt_embeds 0.00024507 65.62379092 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.17414649 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.06205029 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00065593 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00721525 0.50486088 + vae.encoder_f1 0.00721777 0.50413579 + vae.decoder 0.00018707 0.01531457 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 3.46108061 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 261572 +BPFP 0.9255 bits/point +EBPFP 1.8510 equivalent bits/point +MSE 3.461081 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.008s, Pack+Encode: 2.135s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4611 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,304B, BPFP=24.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,936B, BPFP=1.4794 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,588B, BPFP=22.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,152B, BPFP=1.4734 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 57,284B, BPFP=1.4530 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 47,796B, BPFP=0.7293 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 47,872B, BPFP=0.7305 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,652B, BPFP=0.9354 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.14543401 + text_encoder-item0.clip_prompt_embeds 0.00046272 526.81561147 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.16482172 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.05286449 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00063601 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.01999603 0.74573690 + vae.encoder_f1 0.01999529 0.74725121 + vae.decoder 0.00024882 0.01906798 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 15.63575429 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 292540 +BPFP 1.0351 bits/point +EBPFP 2.0702 equivalent bits/point +MSE 15.635754 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 15.6358 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,200B, BPFP=22.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,056B, BPFP=1.4957 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,444B, BPFP=21.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,944B, BPFP=1.5377 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 58,336B, BPFP=1.4797 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 49,812B, BPFP=0.7601 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 49,788B, BPFP=0.7597 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,208B, BPFP=0.5862 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.18092106 + text_encoder-item0.clip_prompt_embeds 0.00020334 75.96278916 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.15722663 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.05110690 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00063831 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.01341345 0.70068753 + vae.encoder_f1 0.01341645 0.69802886 + vae.decoder 0.00018350 0.01297745 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 3.82111293 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 286744 +BPFP 1.0146 bits/point +EBPFP 2.0292 equivalent bits/point +MSE 3.821113 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.610s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8211 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,168B, BPFP=22.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,816B, BPFP=1.4632 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,380B, BPFP=21.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,884B, BPFP=1.6140 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 66,604B, BPFP=1.6894 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 56,412B, BPFP=0.8608 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 56,440B, BPFP=0.8612 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 42,480B, BPFP=1.2964 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.17500885 + text_encoder-item0.clip_prompt_embeds 0.00022316 138.71661086 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.19323925 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.05293774 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00072218 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00606298 0.54114926 + vae.encoder_f1 0.00607096 0.54039824 + vae.decoder 0.00023408 0.02190416 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 5.39003077 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 332140 +BPFP 1.1752 bits/point +EBPFP 2.3504 equivalent bits/point +MSE 5.390031 +---------------------- -------------------------------------------------------- +Time: 3.750s Load: 0.009s, Pack+Encode: 2.140s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.3900 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,208B, BPFP=23.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,996B, BPFP=1.4876 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,512B, BPFP=21.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,388B, BPFP=1.4925 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 64,244B, BPFP=1.6296 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 52,144B, BPFP=0.7957 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 52,124B, BPFP=0.7953 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,196B, BPFP=1.1351 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.17327617 + text_encoder-item0.clip_prompt_embeds 0.00023597 65.35782349 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.17242184 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.04477924 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00066292 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00653100 0.56126887 + vae.encoder_f1 0.00653745 0.56055206 + vae.decoder 0.00020026 0.01968447 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 3.48004479 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 314768 +BPFP 1.1137 bits/point +EBPFP 2.2275 equivalent bits/point +MSE 3.480045 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4800 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,240B, BPFP=23.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,592B, BPFP=1.5682 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,460B, BPFP=21.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,764B, BPFP=1.6042 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,328B, BPFP=1.5302 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 52,208B, BPFP=0.7966 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 52,188B, BPFP=0.7963 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,656B, BPFP=0.7830 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.15267690 + text_encoder-item0.clip_prompt_embeds 0.00022433 46.47950487 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.18215313 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.04840512 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00064631 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00869686 0.72573489 + vae.encoder_f1 0.00870063 0.73106110 + vae.decoder 0.00021246 0.01730566 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 3.06383825 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 301392 +BPFP 1.0664 bits/point +EBPFP 2.1328 equivalent bits/point +MSE 3.063838 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0638 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,248B, BPFP=23.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,796B, BPFP=1.5958 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,452B, BPFP=21.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,220B, BPFP=1.7224 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 65,332B, BPFP=1.6572 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 59,652B, BPFP=0.9102 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 59,628B, BPFP=0.9099 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,804B, BPFP=0.9401 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.17643106 + text_encoder-item0.clip_prompt_embeds 0.00022433 87.19092431 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.17736870 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.04916840 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00069601 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00626512 0.65308589 + vae.encoder_f1 0.00626949 0.65461457 + vae.decoder 0.00018936 0.01765409 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 4.09415408 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 328088 +BPFP 1.1609 bits/point +EBPFP 2.3217 equivalent bits/point +MSE 4.094154 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.009s, Pack+Encode: 2.140s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0942 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,296B, BPFP=23.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,000B, BPFP=1.6234 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,300B, BPFP=20.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,680B, BPFP=1.7597 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,484B, BPFP=1.7371 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,104B, BPFP=0.6425 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,108B, BPFP=0.6425 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,840B, BPFP=0.6665 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.22018689 + text_encoder-item0.clip_prompt_embeds 0.00026137 197.23224432 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.19566448 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.06308743 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00071722 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.35915655 1.47126102 + vae.encoder_f1 0.35915723 1.47830558 + vae.decoder 0.00024181 0.01628034 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 7.35347109 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 287768 +BPFP 1.0182 bits/point +EBPFP 2.0364 equivalent bits/point +MSE 7.353471 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.3535 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,276B, BPFP=23.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,552B, BPFP=1.4275 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,464B, BPFP=21.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,940B, BPFP=1.4562 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 58,180B, BPFP=1.4758 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,624B, BPFP=0.2842 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,624B, BPFP=0.2842 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 29,384B, BPFP=0.8967 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.16812940 + text_encoder-item0.clip_prompt_embeds 0.00021656 105.15539604 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.18997478 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.05421855 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00069842 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.29031765 1.21397209 + vae.encoder_f1 0.29031771 1.21299541 + vae.decoder 0.00019965 0.01969854 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 4.82401491 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 233000 +BPFP 0.8244 bits/point +EBPFP 1.6488 equivalent bits/point +MSE 4.824015 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8240 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,256B, BPFP=23.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,568B, BPFP=1.4297 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,384B, BPFP=21.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,188B, BPFP=1.4763 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,652B, BPFP=1.4370 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 44,520B, BPFP=0.6793 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 44,516B, BPFP=0.6793 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 46,012B, BPFP=1.4042 +⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.17505507 + text_encoder-item0.clip_prompt_embeds 0.00025451 55.85747261 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.18437517 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.05515794 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00057791 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00595764 0.44371745 + vae.encoder_f1 0.00596395 0.44276246 + vae.decoder 0.00019845 0.02220349 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 3.17773231 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 300052 +BPFP 1.0617 bits/point +EBPFP 2.1233 equivalent bits/point +MSE 3.177732 +---------------------- -------------------------------------------------------- +Time: 3.772s Load: 0.009s, Pack+Encode: 2.163s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1777 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,284B, BPFP=23.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,868B, BPFP=1.6055 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,412B, BPFP=21.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,348B, BPFP=1.7328 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,060B, BPFP=1.7517 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,500B, BPFP=0.4959 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,504B, BPFP=0.4960 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,196B, BPFP=0.6469 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.16139813 + text_encoder-item0.clip_prompt_embeds 0.00026157 234.80600649 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.18938017 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.05413896 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00067941 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.40456498 1.71334231 + vae.encoder_f1 0.40456539 1.72682488 + vae.decoder 0.00020503 0.01581192 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 8.44949802 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 268128 +BPFP 0.9487 bits/point +EBPFP 1.8974 equivalent bits/point +MSE 8.449498 +---------------------- -------------------------------------------------------- +Time: 3.761s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.4495 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,192B, BPFP=22.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,996B, BPFP=1.6228 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,464B, BPFP=21.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,484B, BPFP=1.5003 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 58,936B, BPFP=1.4949 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 52,784B, BPFP=0.8054 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 52,788B, BPFP=0.8055 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 41,152B, BPFP=1.2559 +⌛️ [2/4] FRONTEND: Frontend time: 2.165s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.619s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.16524514 + text_encoder-item0.clip_prompt_embeds 0.00027179 34.21484164 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.19483825 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.04771650 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00064736 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00673531 0.67256379 + vae.encoder_f1 0.00673732 0.67427039 + vae.decoder 0.00020129 0.02058178 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 2.71792034 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 315752 +BPFP 1.1172 bits/point +EBPFP 2.2344 equivalent bits/point +MSE 2.717920 +---------------------- -------------------------------------------------------- +Time: 3.792s Load: 0.009s, Pack+Encode: 2.165s, Decode+Unpack: 1.619s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7179 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,304B, BPFP=24.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,148B, BPFP=1.6434 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,472B, BPFP=21.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,196B, BPFP=1.6393 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,712B, BPFP=1.7683 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,792B, BPFP=0.6530 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,780B, BPFP=0.6528 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,020B, BPFP=0.5804 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.609s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.15402593 + text_encoder-item0.clip_prompt_embeds 0.00023057 55.63670184 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.19731729 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.04621179 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00073551 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00881784 0.66788447 + vae.encoder_f1 0.00882136 0.66845489 + vae.decoder 0.00017598 0.01359229 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 3.27490710 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 286380 +BPFP 1.0133 bits/point +EBPFP 2.0266 equivalent bits/point +MSE 3.274907 +---------------------- -------------------------------------------------------- +Time: 3.779s Load: 0.008s, Pack+Encode: 2.161s, Decode+Unpack: 1.609s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2749 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,272B, BPFP=23.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,428B, BPFP=1.5460 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,452B, BPFP=21.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,652B, BPFP=1.5951 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,484B, BPFP=1.6103 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 48,056B, BPFP=0.7333 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 48,056B, BPFP=0.7333 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 46,300B, BPFP=1.4130 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.614s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.15367255 + text_encoder-item0.clip_prompt_embeds 0.00025208 64.74622903 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.18441378 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.04352784 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00073197 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00582247 0.47978622 + vae.encoder_f1 0.00582996 0.47914782 + vae.decoder 0.00016099 0.01978967 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 3.42624514 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 316656 +BPFP 1.1204 bits/point +EBPFP 2.2408 equivalent bits/point +MSE 3.426245 +---------------------- -------------------------------------------------------- +Time: 3.771s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.614s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4262 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,268B, BPFP=23.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,040B, BPFP=1.4935 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,396B, BPFP=21.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,532B, BPFP=1.5854 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,504B, BPFP=1.5347 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 60,000B, BPFP=0.9155 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 60,012B, BPFP=0.9157 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 34,672B, BPFP=1.0581 +⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.609s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.15942344 + text_encoder-item0.clip_prompt_embeds 0.00020809 23.40837434 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.20109148 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.05985707 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00061308 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00602745 0.63162947 + vae.encoder_f1 0.00603159 0.63141286 + vae.decoder 0.00017526 0.01837862 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 2.41611894 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 325380 +BPFP 1.1513 bits/point +EBPFP 2.3026 equivalent bits/point +MSE 2.416119 +---------------------- -------------------------------------------------------- +Time: 3.775s Load: 0.009s, Pack+Encode: 2.158s, Decode+Unpack: 1.609s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4161 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,228B, BPFP=23.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,912B, BPFP=1.4762 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,480B, BPFP=21.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,920B, BPFP=1.5357 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 59,092B, BPFP=1.4989 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 61,380B, BPFP=0.9366 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 61,364B, BPFP=0.9363 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,036B, BPFP=1.1302 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.16331942 + text_encoder-item0.clip_prompt_embeds 0.00020908 34.15304764 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.18280993 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.04340225 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00064143 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00634616 0.62349063 + vae.encoder_f1 0.00635208 0.62344021 + vae.decoder 0.00022721 0.01934971 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 2.69279893 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 328368 +BPFP 1.1619 bits/point +EBPFP 2.3237 equivalent bits/point +MSE 2.692799 +---------------------- -------------------------------------------------------- +Time: 3.767s Load: 0.009s, Pack+Encode: 2.151s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6928 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,348B, BPFP=24.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,908B, BPFP=1.4756 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,548B, BPFP=22.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,192B, BPFP=1.4766 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 57,856B, BPFP=1.4675 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,196B, BPFP=0.4150 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,192B, BPFP=0.4149 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,552B, BPFP=0.4746 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.16804391 + text_encoder-item0.clip_prompt_embeds 0.00022947 67.59145444 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.19786594 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.06590510 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00059310 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.05448642 0.73459268 + vae.encoder_f1 0.05448771 0.73431456 + vae.decoder 0.00017748 0.01334673 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 3.61913793 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 236748 +BPFP 0.8377 bits/point +EBPFP 1.6754 equivalent bits/point +MSE 3.619138 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6191 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,252B, BPFP=23.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,876B, BPFP=1.4713 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,548B, BPFP=22.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,460B, BPFP=1.5795 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 62,184B, BPFP=1.5773 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,240B, BPFP=0.5988 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,252B, BPFP=0.5989 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,796B, BPFP=0.4821 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.611s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.16238896 + text_encoder-item0.clip_prompt_embeds 0.00020169 54.94558729 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.16970854 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.06073993 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00066497 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.06876971 0.87679124 + vae.encoder_f1 0.06877109 0.87631971 + vae.decoder 0.00023999 0.01240053 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 3.35394626 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 266564 +BPFP 0.9432 bits/point +EBPFP 1.8864 equivalent bits/point +MSE 3.353946 +---------------------- -------------------------------------------------------- +Time: 3.760s Load: 0.009s, Pack+Encode: 2.141s, Decode+Unpack: 1.611s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3539 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,288B, BPFP=23.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,212B, BPFP=1.5168 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,472B, BPFP=21.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,244B, BPFP=1.5620 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 59,220B, BPFP=1.5021 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 44,968B, BPFP=0.6862 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 44,972B, BPFP=0.6862 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 46,436B, BPFP=1.4171 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.14490747 + text_encoder-item0.clip_prompt_embeds 0.00025253 43.47165432 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.19683256 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.05724801 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00062697 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00595097 0.45323032 + vae.encoder_f1 0.00595882 0.45258334 + vae.decoder 0.00020134 0.02336799 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 2.85849555 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 305768 +BPFP 1.0819 bits/point +EBPFP 2.1638 equivalent bits/point +MSE 2.858496 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8585 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,260B, BPFP=23.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,468B, BPFP=1.5514 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,484B, BPFP=21.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,548B, BPFP=1.6679 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 62,508B, BPFP=1.5855 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,400B, BPFP=0.5249 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,396B, BPFP=0.5248 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,628B, BPFP=0.9347 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.16726146 + text_encoder-item0.clip_prompt_embeds 0.00022201 43.04717093 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.18828499 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.06209900 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00063149 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00831743 0.59358579 + vae.encoder_f1 0.00831926 0.59582019 + vae.decoder 0.00028593 0.01477363 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 2.91237217 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 273648 +BPFP 0.9682 bits/point +EBPFP 1.9365 equivalent bits/point +MSE 2.912372 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.007s, Pack+Encode: 2.142s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9124 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,260B, BPFP=23.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,068B, BPFP=1.6326 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,396B, BPFP=21.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,448B, BPFP=1.6597 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,016B, BPFP=1.7252 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 60,364B, BPFP=0.9211 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 60,336B, BPFP=0.9207 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 32,532B, BPFP=0.9928 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.16025658 + text_encoder-item0.clip_prompt_embeds 0.00026808 63.35090723 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.19454091 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.05618355 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00070044 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00606586 0.64368868 + vae.encoder_f1 0.00607066 0.64397717 + vae.decoder 0.00019664 0.01783791 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 3.46630728 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 333376 +BPFP 1.1796 bits/point +EBPFP 2.3591 equivalent bits/point +MSE 3.466307 +---------------------- -------------------------------------------------------- +Time: 3.760s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4663 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,212B, BPFP=23.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,916B, BPFP=1.6120 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,400B, BPFP=21.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,044B, BPFP=1.7893 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,256B, BPFP=1.7567 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 44,336B, BPFP=0.6765 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 44,308B, BPFP=0.6761 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 31,492B, BPFP=0.9611 +⌛️ [2/4] FRONTEND: Frontend time: 2.188s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.616s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.18012738 + text_encoder-item0.clip_prompt_embeds 0.00023198 60.15342600 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.22237303 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.05185057 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00069018 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.05216765 0.83568740 + vae.encoder_f1 0.05216896 0.83063221 + vae.decoder 0.00017960 0.01729849 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 3.47025093 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 302920 +BPFP 1.0718 bits/point +EBPFP 2.1436 equivalent bits/point +MSE 3.470251 +---------------------- -------------------------------------------------------- +Time: 3.811s Load: 0.007s, Pack+Encode: 2.188s, Decode+Unpack: 1.616s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4703 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,232B, BPFP=23.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,396B, BPFP=1.5417 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,564B, BPFP=22.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,284B, BPFP=1.5653 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,540B, BPFP=1.5356 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 61,492B, BPFP=0.9383 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 61,480B, BPFP=0.9381 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 35,116B, BPFP=1.0717 +⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.620s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.17191972 + text_encoder-item0.clip_prompt_embeds 0.00023125 128.45260078 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.17190791 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.04627994 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00073300 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00620361 0.58816350 + vae.encoder_f1 0.00620966 0.58889437 + vae.decoder 0.00020748 0.02039239 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 5.14324692 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 329060 +BPFP 1.1643 bits/point +EBPFP 2.3286 equivalent bits/point +MSE 5.143247 +---------------------- -------------------------------------------------------- +Time: 3.783s Load: 0.009s, Pack+Encode: 2.154s, Decode+Unpack: 1.620s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.1432 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,284B, BPFP=23.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,320B, BPFP=1.5314 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,328B, BPFP=20.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,368B, BPFP=1.6532 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,420B, BPFP=1.6087 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 50,200B, BPFP=0.7660 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 50,176B, BPFP=0.7656 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,736B, BPFP=1.0295 +⌛️ [2/4] FRONTEND: Frontend time: 2.192s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.14655912 + text_encoder-item0.clip_prompt_embeds 0.00023066 55.81180668 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.20127709 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.05871335 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00066599 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.03159856 0.65865707 + vae.encoder_f1 0.03160188 0.65870076 + vae.decoder 0.00018417 0.01790682 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 3.27612065 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 308788 +BPFP 1.0926 bits/point +EBPFP 2.1852 equivalent bits/point +MSE 3.276121 +---------------------- -------------------------------------------------------- +Time: 3.803s Load: 0.009s, Pack+Encode: 2.192s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2761 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,292B, BPFP=23.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,644B, BPFP=1.5752 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,376B, BPFP=21.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,760B, BPFP=1.6039 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 65,264B, BPFP=1.6554 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 62,956B, BPFP=0.9606 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 62,960B, BPFP=0.9607 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,824B, BPFP=1.0322 +⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.14700795 + text_encoder-item0.clip_prompt_embeds 0.00024948 43.18783820 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.18315802 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.05394546 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00073411 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.03490865 1.01297677 + vae.encoder_f1 0.03491008 1.00842309 + vae.decoder 0.00028462 0.02266718 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 3.10954173 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 336032 +BPFP 1.1890 bits/point +EBPFP 2.3779 equivalent bits/point +MSE 3.109542 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.136s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1095 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,276B, BPFP=23.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,908B, BPFP=1.4756 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,308B, BPFP=20.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,648B, BPFP=1.5948 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,360B, BPFP=1.7593 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,264B, BPFP=0.4923 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,256B, BPFP=0.4922 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 45,620B, BPFP=1.3922 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.14271660 + text_encoder-item0.clip_prompt_embeds 0.00021560 65.23402834 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.18255272 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.04289608 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00079303 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00544735 0.37008247 + vae.encoder_f1 0.00544843 0.37063712 + vae.decoder 0.00018632 0.02184167 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 3.38861714 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 289596 +BPFP 1.0247 bits/point +EBPFP 2.0493 equivalent bits/point +MSE 3.388617 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3886 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,132B, BPFP=22.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,992B, BPFP=1.4870 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,452B, BPFP=21.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,536B, BPFP=1.5857 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,196B, BPFP=1.5523 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 53,220B, BPFP=0.8121 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 53,228B, BPFP=0.8122 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,472B, BPFP=1.0215 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.20483543 + text_encoder-item0.clip_prompt_embeds 0.00022698 160.29070279 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.18765749 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.05644213 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00064023 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00630479 0.53643835 + vae.encoder_f1 0.00631430 0.53502142 + vae.decoder 0.00018596 0.01781053 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 5.95163330 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 311184 +BPFP 1.1011 bits/point +EBPFP 2.2021 equivalent bits/point +MSE 5.951633 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.9516 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,320B, BPFP=24.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,272B, BPFP=1.5249 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,564B, BPFP=22.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,900B, BPFP=1.5341 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 58,088B, BPFP=1.4734 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 50,904B, BPFP=0.7767 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 50,928B, BPFP=0.7771 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 38,632B, BPFP=1.1790 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.15889954 + text_encoder-item0.clip_prompt_embeds 0.00024643 34.44861633 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.19307135 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.05871894 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00063685 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00612578 0.51922607 + vae.encoder_f1 0.00613243 0.51807952 + vae.decoder 0.00018179 0.01902582 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 2.65255454 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 308564 +BPFP 1.0918 bits/point +EBPFP 2.1836 equivalent bits/point +MSE 2.652555 +---------------------- -------------------------------------------------------- +Time: 3.769s Load: 0.009s, Pack+Encode: 2.153s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6526 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,232B, BPFP=23.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,732B, BPFP=1.7224 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,448B, BPFP=21.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 24,240B, BPFP=1.9675 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 72,348B, BPFP=1.8351 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 15,480B, BPFP=0.2362 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 15,476B, BPFP=0.2361 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 44,832B, BPFP=1.3682 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.17708192 + text_encoder-item0.clip_prompt_embeds 0.00024049 66.92073779 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.19279066 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.05861531 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00067447 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00526071 0.22158346 + vae.encoder_f1 0.00526072 0.22152595 + vae.decoder 0.00016981 0.01986113 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 3.36417832 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 264744 +BPFP 0.9367 bits/point +EBPFP 1.8735 equivalent bits/point +MSE 3.364178 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3642 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,116B, BPFP=22.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,052B, BPFP=1.4951 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,356B, BPFP=20.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,744B, BPFP=1.5214 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,560B, BPFP=1.5615 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 54,960B, BPFP=0.8386 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 54,968B, BPFP=0.8387 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,060B, BPFP=1.1310 +⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.17002666 + text_encoder-item0.clip_prompt_embeds 0.00022843 23.32018990 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.19234788 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.05649476 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00068242 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00622977 0.55642414 + vae.encoder_f1 0.00623684 0.55706924 + vae.decoder 0.00019755 0.01844364 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 2.37900376 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 317772 +BPFP 1.1244 bits/point +EBPFP 2.2487 equivalent bits/point +MSE 2.379004 +---------------------- -------------------------------------------------------- +Time: 3.761s Load: 0.008s, Pack+Encode: 2.154s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3790 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,388B, BPFP=24.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,752B, BPFP=1.5898 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,620B, BPFP=22.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,392B, BPFP=1.5740 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,004B, BPFP=1.5474 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,540B, BPFP=0.5728 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,564B, BPFP=0.5732 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,764B, BPFP=0.8168 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.15694069 + text_encoder-item0.clip_prompt_embeds 0.00026004 56.17268669 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.18005960 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.05662177 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00078440 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00725303 0.51790035 + vae.encoder_f1 0.00725507 0.51715142 + vae.decoder 0.00017991 0.01571170 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 3.21975950 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 273980 +BPFP 0.9694 bits/point +EBPFP 1.9388 equivalent bits/point +MSE 3.219759 +---------------------- -------------------------------------------------------- +Time: 3.760s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2198 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,296B, BPFP=23.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,124B, BPFP=1.6402 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,516B, BPFP=21.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,800B, BPFP=1.6071 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 62,340B, BPFP=1.5813 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,832B, BPFP=0.5925 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,824B, BPFP=0.5924 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,308B, BPFP=0.6503 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.17909304 + text_encoder-item0.clip_prompt_embeds 0.00031748 64.69982752 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.18854072 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.05936792 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00070075 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.42111695 1.48051131 + vae.encoder_f1 0.42111716 1.47197604 + vae.decoder 0.00019827 0.01485208 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 3.88742938 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 272996 +BPFP 0.9659 bits/point +EBPFP 1.9319 equivalent bits/point +MSE 3.887429 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8874 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,212B, BPFP=23.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,824B, BPFP=1.5996 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,300B, BPFP=20.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,672B, BPFP=1.6779 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,724B, BPFP=1.8193 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 46,248B, BPFP=0.7057 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 46,244B, BPFP=0.7056 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,452B, BPFP=0.7767 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.15780667 + text_encoder-item0.clip_prompt_embeds 0.00024951 58.27591484 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.19130405 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.05126611 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00073628 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.10376993 1.30147386 + vae.encoder_f1 0.10377157 1.30479109 + vae.decoder 0.00019787 0.01650715 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 3.63896720 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 301632 +BPFP 1.0673 bits/point +EBPFP 2.1345 equivalent bits/point +MSE 3.638967 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6390 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,292B, BPFP=23.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,372B, BPFP=1.5384 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,344B, BPFP=20.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,976B, BPFP=1.6214 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,412B, BPFP=1.5577 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 48,716B, BPFP=0.7433 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 48,744B, BPFP=0.7438 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,020B, BPFP=0.7330 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.17014019 + text_encoder-item0.clip_prompt_embeds 0.00022350 82.56025095 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.19217231 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.04594270 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00065894 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.01346414 0.72492135 + vae.encoder_f1 0.01346933 0.72484279 + vae.decoder 0.00019243 0.01489186 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 4.00552183 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 293832 +BPFP 1.0397 bits/point +EBPFP 2.0793 equivalent bits/point +MSE 4.005522 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0055 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,312B, BPFP=24.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,520B, BPFP=1.5584 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,572B, BPFP=22.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,768B, BPFP=1.5234 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,312B, BPFP=1.7074 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,752B, BPFP=0.6371 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,752B, BPFP=0.6371 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,104B, BPFP=0.7356 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.14565947 + text_encoder-item0.clip_prompt_embeds 0.00024958 76.70555161 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.20110595 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.04976418 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00070821 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.11196710 1.17707825 + vae.encoder_f1 0.11196851 1.16917825 + vae.decoder 0.00023459 0.01836617 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 4.06084811 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 285048 +BPFP 1.0086 bits/point +EBPFP 2.0172 equivalent bits/point +MSE 4.060848 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0608 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,344B, BPFP=24.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,444B, BPFP=1.6834 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,504B, BPFP=21.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,272B, BPFP=1.6455 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 59,680B, BPFP=1.5138 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 59,604B, BPFP=0.9095 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 59,604B, BPFP=0.9095 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 32,060B, BPFP=0.9784 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.14764025 + text_encoder-item0.clip_prompt_embeds 0.00025929 54.44634825 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.18161049 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.05164745 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00065477 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00675017 0.65804160 + vae.encoder_f1 0.00675421 0.65729773 + vae.decoder 0.00023635 0.02057406 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 3.23992807 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 323468 +BPFP 1.1445 bits/point +EBPFP 2.2890 equivalent bits/point +MSE 3.239928 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2399 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,232B, BPFP=23.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,388B, BPFP=1.5406 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,440B, BPFP=21.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,060B, BPFP=1.5471 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 66,796B, BPFP=1.6943 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 68,600B, BPFP=1.0468 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 68,584B, BPFP=1.0465 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 31,992B, BPFP=0.9763 +⌛️ [2/4] FRONTEND: Frontend time: 2.310s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.14690455 + text_encoder-item0.clip_prompt_embeds 0.00064775 143.37209145 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.20201049 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.01727503 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00073125 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00728993 0.76399535 + vae.encoder_f1 0.00729572 0.76287431 + vae.decoder 0.00026488 0.02097866 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 5.61339226 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 346048 +BPFP 1.2244 bits/point +EBPFP 2.4488 equivalent bits/point +MSE 5.613392 +---------------------- -------------------------------------------------------- +Time: 3.923s Load: 0.009s, Pack+Encode: 2.310s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6134 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,316B, BPFP=24.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,796B, BPFP=1.5958 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,440B, BPFP=21.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,544B, BPFP=1.5864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 65,360B, BPFP=1.6579 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 59,476B, BPFP=0.9075 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 59,460B, BPFP=0.9073 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 34,028B, BPFP=1.0385 +⌛️ [2/4] FRONTEND: Frontend time: 2.170s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.15812045 + text_encoder-item0.clip_prompt_embeds 0.00023188 30.73165669 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.16469988 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.05811748 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00068156 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00613207 0.61146992 + vae.encoder_f1 0.00613899 0.61205173 + vae.decoder 0.00023812 0.01984278 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 2.59857681 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 329376 +BPFP 1.1654 bits/point +EBPFP 2.3308 equivalent bits/point +MSE 2.598577 +---------------------- -------------------------------------------------------- +Time: 3.789s Load: 0.009s, Pack+Encode: 2.170s, Decode+Unpack: 1.610s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5986 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,228B, BPFP=23.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,772B, BPFP=1.5925 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,376B, BPFP=21.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,264B, BPFP=1.6448 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,264B, BPFP=1.6047 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 48,688B, BPFP=0.7429 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 48,692B, BPFP=0.7430 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 29,376B, BPFP=0.8965 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.14377297 + text_encoder-item0.clip_prompt_embeds 0.00023678 56.18664604 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.21778450 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.06202599 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00063112 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00636537 0.61048186 + vae.encoder_f1 0.00636991 0.60996342 + vae.decoder 0.00025538 0.01893326 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 3.26371900 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 301616 +BPFP 1.0672 bits/point +EBPFP 2.1344 equivalent bits/point +MSE 3.263719 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.009s, Pack+Encode: 2.147s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2637 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,368B, BPFP=24.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,384B, BPFP=1.5400 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,404B, BPFP=21.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,488B, BPFP=1.5006 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 59,692B, BPFP=1.5141 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 46,112B, BPFP=0.7036 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 46,116B, BPFP=0.7037 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,140B, BPFP=0.7062 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.15820582 + text_encoder-item0.clip_prompt_embeds 0.00023432 32.52944907 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.16797398 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.03956272 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00070969 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.23155926 1.46278691 + vae.encoder_f1 0.23156048 1.47171021 + vae.decoder 0.00018572 0.01594066 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 3.04109036 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 284660 +BPFP 1.0072 bits/point +EBPFP 2.0144 equivalent bits/point +MSE 3.041090 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0411 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,256B, BPFP=23.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,112B, BPFP=1.5032 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,468B, BPFP=21.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,924B, BPFP=1.5360 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,724B, BPFP=1.5656 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 60,544B, BPFP=0.9238 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 60,544B, BPFP=0.9238 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 38,156B, BPFP=1.1644 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.17466384 + text_encoder-item0.clip_prompt_embeds 0.00022528 68.60819552 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.18699622 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.04594975 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00065757 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00729824 0.65644693 + vae.encoder_f1 0.00730369 0.65518546 + vae.decoder 0.00019938 0.02164071 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 3.60935798 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 330684 +BPFP 1.1700 bits/point +EBPFP 2.3401 equivalent bits/point +MSE 3.609358 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6094 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,240B, BPFP=23.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,864B, BPFP=1.4697 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,560B, BPFP=22.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,988B, BPFP=1.5412 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 57,744B, BPFP=1.4647 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,768B, BPFP=0.6221 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,768B, BPFP=0.6221 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 43,968B, BPFP=1.3418 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.15112911 + text_encoder-item0.clip_prompt_embeds 0.00022149 53.75340740 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.16661683 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.06308012 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00075957 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00564371 0.47970742 + vae.encoder_f1 0.00565042 0.48054156 + vae.decoder 0.00019980 0.02193057 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 3.14012744 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 292856 +BPFP 1.0362 bits/point +EBPFP 2.0724 equivalent bits/point +MSE 3.140127 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.009s, Pack+Encode: 2.147s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1401 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,268B, BPFP=23.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,644B, BPFP=1.5752 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,500B, BPFP=21.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,816B, BPFP=1.6084 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 59,956B, BPFP=1.5208 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,004B, BPFP=0.6562 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,028B, BPFP=0.6566 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 41,428B, BPFP=1.2643 +⌛️ [2/4] FRONTEND: Frontend time: 2.156s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.611s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.14613802 + text_encoder-item0.clip_prompt_embeds 0.00022173 56.99800037 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.17969291 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.06081925 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00066209 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00576096 0.45442855 + vae.encoder_f1 0.00576981 0.45452225 + vae.decoder 0.00019592 0.01975720 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 3.21273576 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 298600 +BPFP 1.0565 bits/point +EBPFP 2.1131 equivalent bits/point +MSE 3.212736 +---------------------- -------------------------------------------------------- +Time: 3.774s Load: 0.008s, Pack+Encode: 2.156s, Decode+Unpack: 1.611s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2127 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,336B, BPFP=24.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,584B, BPFP=1.7024 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,444B, BPFP=21.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,744B, BPFP=1.7649 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,116B, BPFP=1.7785 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,648B, BPFP=0.5592 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,644B, BPFP=0.5591 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,344B, BPFP=0.8345 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.14590506 + text_encoder-item0.clip_prompt_embeds 0.00025917 23.33977400 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.19405370 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.05570461 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00068784 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00594818 0.46699357 + vae.encoder_f1 0.00595328 0.46749210 + vae.decoder 0.00023462 0.01722861 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 2.33782516 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 284816 +BPFP 1.0078 bits/point +EBPFP 2.0155 equivalent bits/point +MSE 2.337825 +---------------------- -------------------------------------------------------- +Time: 3.769s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3378 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,240B, BPFP=23.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,828B, BPFP=1.7354 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,552B, BPFP=22.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 24,372B, BPFP=1.9782 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 73,152B, BPFP=1.8555 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,584B, BPFP=0.4667 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,584B, BPFP=0.4667 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,228B, BPFP=0.3732 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.613s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.15062476 + text_encoder-item0.clip_prompt_embeds 0.00022579 75.14831067 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.16743939 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.07721050 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00066821 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.85445058 2.30711269 + vae.encoder_f1 0.85445166 2.29214311 + vae.decoder 0.00025257 0.01067699 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 4.54283523 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 263496 +BPFP 0.9323 bits/point +EBPFP 1.8646 equivalent bits/point +MSE 4.542835 +---------------------- -------------------------------------------------------- +Time: 3.772s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.613s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.5428 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,312B, BPFP=24.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,092B, BPFP=1.5005 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,408B, BPFP=21.3000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,396B, BPFP=1.5744 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,788B, BPFP=1.5673 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 65,076B, BPFP=0.9930 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 65,060B, BPFP=0.9927 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 41,352B, BPFP=1.2620 +⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.616s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.14680291 + text_encoder-item0.clip_prompt_embeds 0.00025458 53.70972842 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.20335569 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.06220918 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00063900 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00628510 0.61650687 + vae.encoder_f1 0.00629234 0.61684138 + vae.decoder 0.00023521 0.02318382 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 3.20242224 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 343440 +BPFP 1.2152 bits/point +EBPFP 2.4304 equivalent bits/point +MSE 3.202422 +---------------------- -------------------------------------------------------- +Time: 3.787s Load: 0.009s, Pack+Encode: 2.162s, Decode+Unpack: 1.616s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2024 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,296B, BPFP=23.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,392B, BPFP=1.5411 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,584B, BPFP=22.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,888B, BPFP=1.5331 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 64,300B, BPFP=1.6310 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 45,332B, BPFP=0.6917 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 45,328B, BPFP=0.6917 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 36,072B, BPFP=1.1008 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.609s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.18238997 + text_encoder-item0.clip_prompt_embeds 0.00022807 47.31118946 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.17919170 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.04175095 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00070745 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00573429 0.46906576 + vae.encoder_f1 0.00574192 0.46878031 + vae.decoder 0.00017875 0.01781913 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 2.96504106 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 301148 +BPFP 1.0655 bits/point +EBPFP 2.1311 equivalent bits/point +MSE 2.965041 +---------------------- -------------------------------------------------------- +Time: 3.767s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.609s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9650 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,344B, BPFP=24.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 14,260B, BPFP=1.9291 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,456B, BPFP=21.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 24,940B, BPFP=2.0244 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 87,832B, BPFP=2.2279 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 58,616B, BPFP=0.8944 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 58,608B, BPFP=0.8943 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,260B, BPFP=1.1371 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.14257499 + text_encoder-item0.clip_prompt_embeds 0.00027120 43.47874814 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.18615652 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.05469350 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00107247 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00781570 0.70655751 + vae.encoder_f1 0.00781878 0.70689619 + vae.decoder 0.00029724 0.02201514 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 2.97618182 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 361272 +BPFP 1.2783 bits/point +EBPFP 2.5566 equivalent bits/point +MSE 2.976182 +---------------------- -------------------------------------------------------- +Time: 3.772s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9762 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,356B, BPFP=24.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,608B, BPFP=1.5703 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,320B, BPFP=20.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,516B, BPFP=1.6653 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,024B, BPFP=1.5986 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 52,208B, BPFP=0.7966 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 52,208B, BPFP=0.7966 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 44,544B, BPFP=1.3594 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.15226430 + text_encoder-item0.clip_prompt_embeds 0.00022930 34.03493219 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.18299549 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.05869320 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00073607 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00577752 0.48993260 + vae.encoder_f1 0.00578475 0.48937353 + vae.decoder 0.00024190 0.02166146 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 2.62859587 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 323740 +BPFP 1.1455 bits/point +EBPFP 2.2910 equivalent bits/point +MSE 2.628596 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.009s, Pack+Encode: 2.151s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6286 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,396B, BPFP=24.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,264B, BPFP=1.7944 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,588B, BPFP=22.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,168B, BPFP=1.7994 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 72,492B, BPFP=1.8388 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 55,556B, BPFP=0.8477 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 55,564B, BPFP=0.8478 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,600B, BPFP=0.5676 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.615s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.16777096 + text_encoder-item0.clip_prompt_embeds 0.00028764 65.67873208 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.17476597 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.04374508 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00075682 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.03343784 0.96358573 + vae.encoder_f1 0.03344063 0.96850485 + vae.decoder 0.00016139 0.01290431 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 3.67550811 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 317584 +BPFP 1.1237 bits/point +EBPFP 2.2474 equivalent bits/point +MSE 3.675508 +---------------------- -------------------------------------------------------- +Time: 3.775s Load: 0.009s, Pack+Encode: 2.151s, Decode+Unpack: 1.615s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6755 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,156B, BPFP=22.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,744B, BPFP=1.4535 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,360B, BPFP=21.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,856B, BPFP=1.5305 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 62,216B, BPFP=1.5781 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 64,572B, BPFP=0.9853 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 64,584B, BPFP=0.9855 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 39,784B, BPFP=1.2141 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.615s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.19762673 + text_encoder-item0.clip_prompt_embeds 0.00023094 179.51110998 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.19078257 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.05406885 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00065579 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00637455 0.62283111 + vae.encoder_f1 0.00637988 0.62169218 + vae.decoder 0.00020059 0.02027907 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 6.49465587 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 340228 +BPFP 1.2038 bits/point +EBPFP 2.4076 equivalent bits/point +MSE 6.494656 +---------------------- -------------------------------------------------------- +Time: 3.777s Load: 0.009s, Pack+Encode: 2.153s, Decode+Unpack: 1.615s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.4947 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,264B, BPFP=23.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,592B, BPFP=1.5682 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,580B, BPFP=22.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,944B, BPFP=1.6188 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 64,668B, BPFP=1.6403 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 51,336B, BPFP=0.7833 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 51,356B, BPFP=0.7836 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 38,960B, BPFP=1.1890 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.19460050 + text_encoder-item0.clip_prompt_embeds 0.00025217 42.83069535 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.18787322 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.04970380 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00062938 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00581597 0.50067455 + vae.encoder_f1 0.00582356 0.50037801 + vae.decoder 0.00019494 0.01984366 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 2.86309038 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 317656 +BPFP 1.1240 bits/point +EBPFP 2.2479 equivalent bits/point +MSE 2.863090 +---------------------- -------------------------------------------------------- +Time: 3.767s Load: 0.009s, Pack+Encode: 2.152s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.8631 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,276B, BPFP=23.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,876B, BPFP=1.4713 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,512B, BPFP=21.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,680B, BPFP=1.4351 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,436B, BPFP=1.5330 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,544B, BPFP=0.4355 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,548B, BPFP=0.4356 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 31,784B, BPFP=0.9700 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.19348019 + text_encoder-item0.clip_prompt_embeds 0.00026975 53.81836783 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.17603602 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.05556345 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00069523 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 1.11695218 2.48088098 + vae.encoder_f1 1.11695278 2.47955489 + vae.decoder 0.00019720 0.01899914 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 4.06874925 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 257612 +BPFP 0.9115 bits/point +EBPFP 1.8230 equivalent bits/point +MSE 4.068749 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0687 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,284B, BPFP=23.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,788B, BPFP=1.5947 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,352B, BPFP=20.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,480B, BPFP=1.5812 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 65,388B, BPFP=1.6586 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 51,340B, BPFP=0.7834 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 51,340B, BPFP=0.7834 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 32,912B, BPFP=1.0044 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.15367620 + text_encoder-item0.clip_prompt_embeds 0.00025545 42.92855875 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.15926769 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.05507268 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00068132 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.01535016 0.75488424 + vae.encoder_f1 0.01535382 0.75678027 + vae.decoder 0.00021460 0.01936697 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 2.98420868 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 311840 +BPFP 1.1034 bits/point +EBPFP 2.2067 equivalent bits/point +MSE 2.984209 +---------------------- -------------------------------------------------------- +Time: 3.765s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9842 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,316B, BPFP=24.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,068B, BPFP=1.6326 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,464B, BPFP=21.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,584B, BPFP=1.7519 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,368B, BPFP=1.7849 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 44,744B, BPFP=0.6827 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 44,752B, BPFP=0.6829 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 49,256B, BPFP=1.5032 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.15662664 + text_encoder-item0.clip_prompt_embeds 0.00022628 64.49747616 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.20211825 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.05832563 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00069903 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00589589 0.45638311 + vae.encoder_f1 0.00590398 0.45689851 + vae.decoder 0.00017838 0.02207431 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 3.41006934 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 322508 +BPFP 1.1411 bits/point +EBPFP 2.2822 equivalent bits/point +MSE 3.410069 +---------------------- -------------------------------------------------------- +Time: 3.769s Load: 0.009s, Pack+Encode: 2.150s, Decode+Unpack: 1.610s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4101 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,316B, BPFP=24.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,520B, BPFP=1.5584 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,412B, BPFP=21.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,320B, BPFP=1.5682 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 58,952B, BPFP=1.4953 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 63,188B, BPFP=0.9642 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 63,204B, BPFP=0.9644 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,444B, BPFP=0.8070 +⌛️ [2/4] FRONTEND: Frontend time: 2.169s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.14034181 + text_encoder-item0.clip_prompt_embeds 0.00031548 44.63247430 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.19037170 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.06561415 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00072936 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00725484 0.70919484 + vae.encoder_f1 0.00725992 0.70843613 + vae.decoder 0.00019960 0.01708962 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 3.00718478 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 322312 +BPFP 1.1404 bits/point +EBPFP 2.2809 equivalent bits/point +MSE 3.007185 +---------------------- -------------------------------------------------------- +Time: 3.781s Load: 0.009s, Pack+Encode: 2.169s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0072 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,276B, BPFP=23.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,884B, BPFP=1.4724 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,772B, BPFP=23.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,900B, BPFP=1.4529 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,424B, BPFP=1.4312 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 52,412B, BPFP=0.7997 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 52,400B, BPFP=0.7996 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,444B, BPFP=0.7155 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.611s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.15820683 + text_encoder-item0.clip_prompt_embeds 0.00021831 179.33796672 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 0.17963928 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.05110688 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00062886 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00923516 0.69015270 + vae.encoder_f1 0.00923823 0.68631876 + vae.decoder 0.00019521 0.01426383 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 6.51987402 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 293468 +BPFP 1.0384 bits/point +EBPFP 2.0767 equivalent bits/point +MSE 6.519874 +---------------------- -------------------------------------------------------- +Time: 3.770s Load: 0.009s, Pack+Encode: 2.149s, Decode+Unpack: 1.611s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.5199 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,252B, BPFP=23.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,188B, BPFP=1.5135 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,432B, BPFP=21.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,900B, BPFP=1.6153 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 65,060B, BPFP=1.6503 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 55,256B, BPFP=0.8431 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 55,252B, BPFP=0.8431 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 28,752B, BPFP=0.8774 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.17915984 + text_encoder-item0.clip_prompt_embeds 0.00062166 422.93310335 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.18623586 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.04606338 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00066977 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00831779 0.69413573 + vae.encoder_f1 0.00832197 0.69305187 + vae.decoder 0.00023271 0.01771433 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 12.89376000 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 315048 +BPFP 1.1147 bits/point +EBPFP 2.2294 equivalent bits/point +MSE 12.893760 +---------------------- -------------------------------------------------------- +Time: 3.759s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 12.8938 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,316B, BPFP=24.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,552B, BPFP=1.5628 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,284B, BPFP=20.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,036B, BPFP=1.5451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,100B, BPFP=1.6005 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 46,988B, BPFP=0.7170 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 46,968B, BPFP=0.7167 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 32,520B, BPFP=0.9924 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.15985668 + text_encoder-item0.clip_prompt_embeds 0.00022938 56.10103828 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.20092483 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.05025087 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00075474 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00626977 0.53709018 + vae.encoder_f1 0.00627489 0.53582978 + vae.decoder 0.00017842 0.01888705 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 3.22676568 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 299720 +BPFP 1.0605 bits/point +EBPFP 2.1210 equivalent bits/point +MSE 3.226766 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2268 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,256B, BPFP=23.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,444B, BPFP=1.5482 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,256B, BPFP=20.3500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,560B, BPFP=1.6688 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 64,812B, BPFP=1.6440 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 53,272B, BPFP=0.8129 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 53,252B, BPFP=0.8126 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 46,592B, BPFP=1.4219 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.608s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.13282778 + text_encoder-item0.clip_prompt_embeds 0.00022180 119.06301576 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.19760857 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.05408633 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00063614 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00585720 0.52103591 + vae.encoder_f1 0.00586586 0.52073389 + vae.decoder 0.00016520 0.02001363 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 4.86657634 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 329400 +BPFP 1.1655 bits/point +EBPFP 2.3310 equivalent bits/point +MSE 4.866576 +---------------------- -------------------------------------------------------- +Time: 3.768s Load: 0.009s, Pack+Encode: 2.150s, Decode+Unpack: 1.608s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8666 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,248B, BPFP=23.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,908B, BPFP=1.4756 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,100B, BPFP=19.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,508B, BPFP=1.5834 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,360B, BPFP=1.6071 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,708B, BPFP=0.5754 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,708B, BPFP=0.5754 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,808B, BPFP=0.6350 +⌛️ [2/4] FRONTEND: Frontend time: 2.160s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.609s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.18807779 + text_encoder-item0.clip_prompt_embeds 0.00025784 20.58772786 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.18339553 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.04621671 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00063706 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00734802 0.55913723 + vae.encoder_f1 0.00734987 0.55737591 + vae.decoder 0.00018093 0.01249202 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 2.30709340 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 269304 +BPFP 0.9529 bits/point +EBPFP 1.9057 equivalent bits/point +MSE 2.307093 +---------------------- -------------------------------------------------------- +Time: 3.777s Load: 0.008s, Pack+Encode: 2.160s, Decode+Unpack: 1.609s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3071 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,260B, BPFP=23.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,080B, BPFP=1.6342 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,360B, BPFP=21.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,256B, BPFP=1.6442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,536B, BPFP=1.5609 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 61,592B, BPFP=0.9398 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 61,596B, BPFP=0.9399 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,032B, BPFP=1.1301 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.608s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.16339815 + text_encoder-item0.clip_prompt_embeds 0.00023510 99.28581575 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.17870212 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.05787637 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00066755 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00637359 0.62979424 + vae.encoder_f1 0.00637830 0.62912381 + vae.decoder 0.00018566 0.02011509 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 4.39984008 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 333668 +BPFP 1.1806 bits/point +EBPFP 2.3612 equivalent bits/point +MSE 4.399840 +---------------------- -------------------------------------------------------- +Time: 3.777s Load: 0.009s, Pack+Encode: 2.161s, Decode+Unpack: 1.608s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.3998 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,312B, BPFP=24.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,208B, BPFP=1.5162 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,516B, BPFP=21.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,036B, BPFP=1.6263 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 64,776B, BPFP=1.6431 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,024B, BPFP=0.6565 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,028B, BPFP=0.6566 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,740B, BPFP=0.7245 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.15664427 + text_encoder-item0.clip_prompt_embeds 0.00026418 140.35617898 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.16684606 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.01931407 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00068394 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.01530954 0.71514499 + vae.encoder_f1 0.01531230 0.71620607 + vae.decoder 0.00017892 0.01463184 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 5.51169198 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 285596 +BPFP 1.0105 bits/point +EBPFP 2.0210 equivalent bits/point +MSE 5.511692 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.5117 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,236B, BPFP=23.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,792B, BPFP=1.5952 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,380B, BPFP=21.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,680B, BPFP=1.6786 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,584B, BPFP=1.7143 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 60,104B, BPFP=0.9171 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 60,080B, BPFP=0.9167 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 35,352B, BPFP=1.0789 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.14075704 + text_encoder-item0.clip_prompt_embeds 0.00021481 119.67242458 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.18261530 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.05563975 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00073410 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00591154 0.62185925 + vae.encoder_f1 0.00591973 0.62146735 + vae.decoder 0.00025286 0.01981688 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 4.92930594 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 335164 +BPFP 1.1859 bits/point +EBPFP 2.3718 equivalent bits/point +MSE 4.929306 +---------------------- -------------------------------------------------------- +Time: 3.762s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.9293 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,296B, BPFP=23.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,192B, BPFP=1.6494 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,328B, BPFP=20.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,732B, BPFP=1.7640 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,796B, BPFP=1.7958 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,668B, BPFP=0.5443 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,668B, BPFP=0.5443 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,868B, BPFP=1.1556 +⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.16581404 + text_encoder-item0.clip_prompt_embeds 0.00023458 55.70361117 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.21788883 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.06235682 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00076691 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00588703 0.41212189 + vae.encoder_f1 0.00589573 0.41325063 + vae.decoder 0.00053402 0.02049440 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 3.15969611 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 293504 +BPFP 1.0385 bits/point +EBPFP 2.0770 equivalent bits/point +MSE 3.159696 +---------------------- -------------------------------------------------------- +Time: 3.775s Load: 0.009s, Pack+Encode: 2.162s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1597 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,244B, BPFP=23.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,856B, BPFP=1.6039 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,308B, BPFP=20.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,900B, BPFP=1.6964 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,032B, BPFP=1.7256 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 52,360B, BPFP=0.7990 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 52,364B, BPFP=0.7990 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,068B, BPFP=0.7345 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.17310770 + text_encoder-item0.clip_prompt_embeds 0.00022882 182.13790246 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.21414397 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.05591573 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00065653 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00659691 0.60191941 + vae.encoder_f1 0.00660300 0.60415864 + vae.decoder 0.00023739 0.01636362 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 6.55407605 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 309088 +BPFP 1.0936 bits/point +EBPFP 2.1873 equivalent bits/point +MSE 6.554076 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.009s, Pack+Encode: 2.138s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.5541 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,264B, BPFP=23.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,588B, BPFP=1.5676 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,480B, BPFP=21.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,600B, BPFP=1.5909 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,404B, BPFP=1.5575 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,020B, BPFP=0.6564 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,016B, BPFP=0.6564 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 46,672B, BPFP=1.4243 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.628s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.15927272 + text_encoder-item0.clip_prompt_embeds 0.00023928 53.67016707 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.18524423 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.05394920 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00064152 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00583864 0.43363869 + vae.encoder_f1 0.00583800 0.43370426 + vae.decoder 0.00018889 0.02147261 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 3.11595259 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 305000 +BPFP 1.0792 bits/point +EBPFP 2.1583 equivalent bits/point +MSE 3.115953 +---------------------- -------------------------------------------------------- +Time: 3.776s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.628s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1160 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,292B, BPFP=23.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,872B, BPFP=1.6061 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,152B, BPFP=19.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,748B, BPFP=1.6841 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,520B, BPFP=1.7888 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,420B, BPFP=0.5405 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,404B, BPFP=0.5402 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 39,408B, BPFP=1.2026 +⌛️ [2/4] FRONTEND: Frontend time: 2.159s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.15821056 + text_encoder-item0.clip_prompt_embeds 0.00024821 85.31078362 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.16694061 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.05649911 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00081404 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00570467 0.38725421 + vae.encoder_f1 0.00570488 0.38774273 + vae.decoder 0.00017302 0.01950349 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 3.92199210 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 292772 +BPFP 1.0359 bits/point +EBPFP 2.0718 equivalent bits/point +MSE 3.921992 +---------------------- -------------------------------------------------------- +Time: 3.780s Load: 0.009s, Pack+Encode: 2.159s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9220 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,292B, BPFP=23.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,596B, BPFP=1.5687 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,320B, BPFP=20.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,288B, BPFP=1.6468 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,732B, BPFP=1.6166 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,180B, BPFP=0.5063 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,152B, BPFP=0.5059 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,700B, BPFP=0.6012 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.609s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.16180082 + text_encoder-item0.clip_prompt_embeds 0.00021458 43.41963440 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.15277643 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.05794355 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00066016 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00914783 0.62959158 + vae.encoder_f1 0.00914958 0.62924743 + vae.decoder 0.00017527 0.01320085 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 2.93783287 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 261216 +BPFP 0.9243 bits/point +EBPFP 1.8485 equivalent bits/point +MSE 2.937833 +---------------------- -------------------------------------------------------- +Time: 3.765s Load: 0.007s, Pack+Encode: 2.149s, Decode+Unpack: 1.609s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9378 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,292B, BPFP=23.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,044B, BPFP=1.7646 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,404B, BPFP=21.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,840B, BPFP=1.8539 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 72,060B, BPFP=1.8278 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,560B, BPFP=0.6342 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,580B, BPFP=0.6345 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 46,588B, BPFP=1.4218 +⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.616s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.15767978 + text_encoder-item0.clip_prompt_embeds 0.00022150 53.65137987 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.20873795 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.05679515 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00079742 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00578482 0.43088913 + vae.encoder_f1 0.00579739 0.43026626 + vae.decoder 0.00017668 0.02062439 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 3.11408664 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 317324 +BPFP 1.1228 bits/point +EBPFP 2.2456 equivalent bits/point +MSE 3.114087 +---------------------- -------------------------------------------------------- +Time: 3.779s Load: 0.008s, Pack+Encode: 2.154s, Decode+Unpack: 1.616s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1141 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,176B, BPFP=22.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,164B, BPFP=1.5103 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,428B, BPFP=21.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,208B, BPFP=1.5591 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,944B, BPFP=1.5459 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,276B, BPFP=0.5993 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,272B, BPFP=0.5992 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 34,128B, BPFP=1.0415 +⌛️ [2/4] FRONTEND: Frontend time: 2.166s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.18954796 + text_encoder-item0.clip_prompt_embeds 0.00023894 78.18363603 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.19694004 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.05081510 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00064416 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00958025 0.59536600 + vae.encoder_f1 0.00958229 0.59262460 + vae.decoder 0.00019995 0.01933042 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 3.83108507 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 283552 +BPFP 1.0033 bits/point +EBPFP 2.0066 equivalent bits/point +MSE 3.831085 +---------------------- -------------------------------------------------------- +Time: 3.774s Load: 0.008s, Pack+Encode: 2.166s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8311 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,244B, BPFP=23.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,988B, BPFP=1.7570 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,440B, BPFP=21.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 24,072B, BPFP=1.9539 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,840B, BPFP=1.8222 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,240B, BPFP=0.5225 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,264B, BPFP=0.5228 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 48,564B, BPFP=1.4821 +⌛️ [2/4] FRONTEND: Frontend time: 2.156s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.15380625 + text_encoder-item0.clip_prompt_embeds 0.00023387 136.73048566 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.18595668 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.06135110 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00078498 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00567713 0.39475328 + vae.encoder_f1 0.00567905 0.39397302 + vae.decoder 0.00019376 0.02362059 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 5.27074651 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 305608 +BPFP 1.0813 bits/point +EBPFP 2.1626 equivalent bits/point +MSE 5.270747 +---------------------- -------------------------------------------------------- +Time: 3.770s Load: 0.008s, Pack+Encode: 2.156s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.2707 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,288B, BPFP=23.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,652B, BPFP=1.5763 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,412B, BPFP=21.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,816B, BPFP=1.6084 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,664B, BPFP=1.7670 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,616B, BPFP=0.6045 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,608B, BPFP=0.6044 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,640B, BPFP=0.8435 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.14564219 + text_encoder-item0.clip_prompt_embeds 0.00024281 61.95745400 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.17362514 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.05332747 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00066898 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.02387581 0.64126581 + vae.encoder_f1 0.02387858 0.64355707 + vae.decoder 0.00018648 0.01516625 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 3.42874697 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 287652 +BPFP 1.0178 bits/point +EBPFP 2.0356 equivalent bits/point +MSE 3.428747 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4287 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,368B, BPFP=24.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,544B, BPFP=1.5617 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,328B, BPFP=20.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,660B, BPFP=1.5958 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,292B, BPFP=1.6054 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 61,916B, BPFP=0.9448 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 61,900B, BPFP=0.9445 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,820B, BPFP=0.6659 +⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.609s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.14379510 + text_encoder-item0.clip_prompt_embeds 0.00022399 11.37072278 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.18890476 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.04526055 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00065532 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.01169517 0.81736290 + vae.encoder_f1 0.01169969 0.81258380 + vae.decoder 0.00021186 0.01532731 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 2.18535837 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 319784 +BPFP 1.1315 bits/point +EBPFP 2.2630 equivalent bits/point +MSE 2.185358 +---------------------- -------------------------------------------------------- +Time: 3.775s Load: 0.009s, Pack+Encode: 2.158s, Decode+Unpack: 1.609s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.1854 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,264B, BPFP=23.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,420B, BPFP=1.5449 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,368B, BPFP=21.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,848B, BPFP=1.6110 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,392B, BPFP=1.8109 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 48,500B, BPFP=0.7401 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 48,500B, BPFP=0.7401 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 34,400B, BPFP=1.0498 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.14856638 + text_encoder-item0.clip_prompt_embeds 0.00022123 407.71530032 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.18398854 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.05587563 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00068811 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.32749966 1.93270302 + vae.encoder_f1 0.32750070 1.92354023 + vae.decoder 0.00039956 0.02111486 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 13.06908755 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 313648 +BPFP 1.1098 bits/point +EBPFP 2.2195 equivalent bits/point +MSE 13.069088 +---------------------- -------------------------------------------------------- +Time: 3.760s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 13.0691 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,176B, BPFP=22.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,504B, BPFP=1.5563 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,336B, BPFP=20.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,684B, BPFP=1.6789 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,480B, BPFP=1.5341 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,824B, BPFP=0.6382 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,800B, BPFP=0.6378 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 35,600B, BPFP=1.0864 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.16041439 + text_encoder-item0.clip_prompt_embeds 0.00024675 67.10553216 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.18374945 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.04894915 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00065151 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00566967 0.43980837 + vae.encoder_f1 0.00567867 0.43960518 + vae.decoder 0.00017839 0.01868597 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 3.46961197 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 291360 +BPFP 1.0309 bits/point +EBPFP 2.0618 equivalent bits/point +MSE 3.469612 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4696 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,268B, BPFP=23.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,680B, BPFP=1.4448 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,584B, BPFP=22.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,428B, BPFP=1.4958 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 62,908B, BPFP=1.5957 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,300B, BPFP=0.6302 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,312B, BPFP=0.6304 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 51,280B, BPFP=1.5649 +⌛️ [2/4] FRONTEND: Frontend time: 2.160s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.609s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.13417966 + text_encoder-item0.clip_prompt_embeds 0.00022364 96.43557224 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.19375907 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.05494312 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00064217 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00580750 0.42359689 + vae.encoder_f1 0.00580664 0.42391086 + vae.decoder 0.00018044 0.02222184 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 4.23000423 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 305716 +BPFP 1.0817 bits/point +EBPFP 2.1634 equivalent bits/point +MSE 4.230004 +---------------------- -------------------------------------------------------- +Time: 3.778s Load: 0.009s, Pack+Encode: 2.160s, Decode+Unpack: 1.609s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2300 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,232B, BPFP=23.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,992B, BPFP=1.4870 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,352B, BPFP=20.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,164B, BPFP=1.5555 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 60,824B, BPFP=1.5428 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,580B, BPFP=0.6650 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,560B, BPFP=0.6647 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 28,764B, BPFP=0.8778 +⌛️ [2/4] FRONTEND: Frontend time: 3.383s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.17671879 + text_encoder-item0.clip_prompt_embeds 0.00030118 488.85650027 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.17056533 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.04982888 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00067525 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.03869025 0.81964707 + vae.encoder_f1 0.03869358 0.81821758 + vae.decoder 0.00021614 0.01830192 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 14.67633063 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 286424 +BPFP 1.0134 bits/point +EBPFP 2.0269 equivalent bits/point +MSE 14.676331 +---------------------- -------------------------------------------------------- +Time: 5.002s Load: 0.009s, Pack+Encode: 3.383s, Decode+Unpack: 1.610s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 14.6763 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,232B, BPFP=23.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,152B, BPFP=1.5087 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,452B, BPFP=21.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,844B, BPFP=1.5295 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 61,244B, BPFP=1.5535 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 60,888B, BPFP=0.9291 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 60,856B, BPFP=0.9286 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,672B, BPFP=0.8445 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.18397707 + text_encoder-item0.clip_prompt_embeds 0.00023260 43.35973857 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.19491327 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.05126536 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00073655 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00839879 0.76181096 + vae.encoder_f1 0.00840224 0.76436156 + vae.decoder 0.00019463 0.01746907 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 2.99850248 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 320296 +BPFP 1.1333 bits/point +EBPFP 2.2666 equivalent bits/point +MSE 2.998502 +---------------------- -------------------------------------------------------- +Time: 3.772s Load: 0.009s, Pack+Encode: 2.151s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9985 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,264B, BPFP=23.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,660B, BPFP=1.7127 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,484B, BPFP=21.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,792B, BPFP=1.8500 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,236B, BPFP=1.7562 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 55,320B, BPFP=0.8441 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 55,300B, BPFP=0.8438 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,308B, BPFP=0.7418 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.610s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.15710566 + text_encoder-item0.clip_prompt_embeds 0.00023544 75.33776380 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.18074419 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.04546070 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00086737 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.01160815 0.71895570 + vae.encoder_f1 0.01161249 0.71370322 + vae.decoder 0.00021720 0.01642360 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 3.81282679 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 319320 +BPFP 1.1298 bits/point +EBPFP 2.2597 equivalent bits/point +MSE 3.812827 +---------------------- -------------------------------------------------------- +Time: 3.764s Load: 0.009s, Pack+Encode: 2.144s, Decode+Unpack: 1.610s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8128 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,304B, BPFP=24.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,708B, BPFP=1.5839 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,400B, BPFP=21.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,084B, BPFP=1.6302 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,696B, BPFP=1.6157 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,448B, BPFP=0.5562 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,440B, BPFP=0.5560 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,820B, BPFP=0.6354 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.612s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.14456732 + text_encoder-item0.clip_prompt_embeds 0.00022923 66.36008945 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.18278028 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.05339306 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00065419 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.02989292 0.83714265 + vae.encoder_f1 0.02989391 0.83703035 + vae.decoder 0.00034944 0.01517834 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 3.63418852 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 268856 +BPFP 0.9513 bits/point +EBPFP 1.9026 equivalent bits/point +MSE 3.634189 +---------------------- -------------------------------------------------------- +Time: 3.764s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.612s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6342 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,360B, BPFP=24.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,984B, BPFP=1.6212 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,580B, BPFP=22.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,180B, BPFP=1.6380 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 64,548B, BPFP=1.6373 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 8,396B, BPFP=1.1358 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,400B, BPFP=27.5000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 16,268B, BPFP=1.3205 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 42,292B, BPFP=1.0727 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 57,180B, BPFP=0.8725 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 57,220B, BPFP=0.8731 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 35,304B, BPFP=1.0774 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.609s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.16567891 + text_encoder-item0.clip_prompt_embeds 0.00024627 211.64456507 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.17155350 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.04590938 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00069433 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 + text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 + vae.encoder_f0 0.00613025 0.55162370 + vae.encoder_f1 0.00613536 0.55013245 + vae.decoder 0.00018697 0.01967573 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 7.30155548 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 326312 +BPFP 1.1546 bits/point +EBPFP 2.3092 equivalent bits/point +MSE 7.301555 +---------------------- -------------------------------------------------------- +Time: 3.779s Load: 0.009s, Pack+Encode: 2.161s, Decode+Unpack: 1.609s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.3016 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.0720 bits/point +Avg EBPFP 2.1441 equivalent bits/point +Avg MSE 4.360150 +Avg Time 3.789s +------------------------ ---------------------------- diff --git a/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000361180.zst b/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000361180.zst new file mode 100644 index 0000000000000000000000000000000000000000..95d97cc78199ea5bf6d570f8a82b5f66c78a69bc --- /dev/null +++ b/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000361180.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000551780.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5211887bd34ba0c99681c3980aa62082974dabb9bfe920145222d10d580828a9 +size 2602613 diff --git a/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log b/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..09235da41a28e931f7a308d77d6252432064f970 --- /dev/null +++ b/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log @@ -0,0 +1,21862 @@ +Experiment: dtufc_hyperprior-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: sd35 + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 598 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,340B, BPFP=24.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,680B, BPFP=1.8506 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,544B, BPFP=22.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,356B, BPFP=1.8958 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 76,104B, BPFP=1.9304 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 65,512B, BPFP=0.9996 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 65,512B, BPFP=0.9996 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 36,588B, BPFP=1.1166 +⌛️ [2/4] FRONTEND: Frontend time: 0.701s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.50440510 + text_encoder-item0.clip_prompt_embeds 0.00025464 23.74437314 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.46889367 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.08079600 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00144651 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00635250 0.72601670 + vae.encoder_f1 0.00635834 0.72614378 + vae.decoder 0.00019940 0.01955165 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 2.54451194 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 373700 +BPFP 1.3223 bits/point +EBPFP 2.6445 equivalent bits/point +MSE 2.544512 +---------------------- -------------------------------------------------------- +Time: 1.233s Load: 0.009s, Pack+Encode: 0.701s, Decode+Unpack: 0.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5445 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,436B, BPFP=25.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,600B, BPFP=1.8398 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,752B, BPFP=23.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,376B, BPFP=1.8974 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 74,032B, BPFP=1.8778 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 51,180B, BPFP=0.7809 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 51,180B, BPFP=0.7809 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 32,572B, BPFP=0.9940 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.47676484 + text_encoder-item0.clip_prompt_embeds 0.00022609 48.18833705 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.52637906 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.08576198 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00130585 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.01130640 1.16958404 + vae.encoder_f1 0.01130902 1.16797936 + vae.decoder 0.00020860 0.01948095 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 3.38936379 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 339192 +BPFP 1.2002 bits/point +EBPFP 2.4003 equivalent bits/point +MSE 3.389364 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3894 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,408B, BPFP=25.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,228B, BPFP=1.6542 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,924B, BPFP=24.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,828B, BPFP=1.7718 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,820B, BPFP=1.7203 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,792B, BPFP=0.4241 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,796B, BPFP=0.4241 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,664B, BPFP=0.7832 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.50668462 + text_encoder-item0.clip_prompt_embeds 0.00022402 131.20713271 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 0.63358088 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.07568091 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00111101 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 1.19630027 4.54559326 + vae.encoder_f1 1.19630098 4.54593372 + vae.decoder 0.00023596 0.01884164 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 7.12637795 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 276524 +BPFP 0.9784 bits/point +EBPFP 1.9568 equivalent bits/point +MSE 7.126378 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.1264 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,344B, BPFP=24.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,580B, BPFP=1.8371 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,604B, BPFP=22.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,408B, BPFP=1.9000 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 72,128B, BPFP=1.8295 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 60,936B, BPFP=0.9298 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 60,940B, BPFP=0.9299 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 47,840B, BPFP=1.4600 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.48439340 + text_encoder-item0.clip_prompt_embeds 0.00030342 84.40882035 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.49871812 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.08258602 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00136273 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00586287 0.57886183 + vae.encoder_f1 0.00587438 0.57883632 + vae.decoder 0.00017677 0.02621743 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 4.06375212 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 371844 +BPFP 1.3157 bits/point +EBPFP 2.6314 equivalent bits/point +MSE 4.063752 +---------------------- -------------------------------------------------------- +Time: 0.760s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0638 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,416B, BPFP=25.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,244B, BPFP=1.6564 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,816B, BPFP=23.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,068B, BPFP=1.7912 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,352B, BPFP=1.7084 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,688B, BPFP=0.6666 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,688B, BPFP=0.6666 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 28,492B, BPFP=0.8695 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.50218093 + text_encoder-item0.clip_prompt_embeds 0.00024120 191.22353558 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.52736516 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.07966633 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00113748 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00779453 0.79382467 + vae.encoder_f1 0.00779802 0.79430085 + vae.decoder 0.00023829 0.01994855 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 6.95642537 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 310828 +BPFP 1.0998 bits/point +EBPFP 2.1996 equivalent bits/point +MSE 6.956425 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.9564 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,332B, BPFP=24.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,452B, BPFP=1.8198 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,792B, BPFP=23.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,472B, BPFP=1.8240 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,572B, BPFP=1.7647 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 64,804B, BPFP=0.9888 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 64,800B, BPFP=0.9888 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 34,724B, BPFP=1.0597 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.47535769 + text_encoder-item0.clip_prompt_embeds 0.00025651 23.98524587 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.53364406 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.11392257 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00144920 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00655775 0.86760908 + vae.encoder_f1 0.00656268 0.86729401 + vae.decoder 0.00020283 0.02016536 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 2.61791780 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 363012 +BPFP 1.2844 bits/point +EBPFP 2.5689 equivalent bits/point +MSE 2.617918 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6179 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,364B, BPFP=24.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,716B, BPFP=1.5850 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,836B, BPFP=23.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,748B, BPFP=1.6841 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 64,328B, BPFP=1.6317 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 64,652B, BPFP=0.9865 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 64,656B, BPFP=0.9866 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 43,308B, BPFP=1.3217 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.50844034 + text_encoder-item0.clip_prompt_embeds 0.00022242 167.91935538 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.50561209 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.07228813 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00114281 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00593415 0.65065122 + vae.encoder_f1 0.00594307 0.65037298 + vae.decoder 0.00018992 0.02479925 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 6.28056417 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 362672 +BPFP 1.2832 bits/point +EBPFP 2.5665 equivalent bits/point +MSE 6.280564 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.2806 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,356B, BPFP=24.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,296B, BPFP=1.7987 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,656B, BPFP=22.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,924B, BPFP=1.8607 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,984B, BPFP=1.8259 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 55,832B, BPFP=0.8519 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 55,832B, BPFP=0.8519 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 31,436B, BPFP=0.9594 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.49713059 + text_encoder-item0.clip_prompt_embeds 0.00022110 96.40587798 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.50203609 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.08834902 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00124337 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00641770 0.75440943 + vae.encoder_f1 0.00642053 0.75393313 + vae.decoder 0.00017498 0.01638398 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 4.45794337 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 344380 +BPFP 1.2185 bits/point +EBPFP 2.4370 equivalent bits/point +MSE 4.457943 +---------------------- -------------------------------------------------------- +Time: 0.783s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4579 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,340B, BPFP=24.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,776B, BPFP=1.5931 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,864B, BPFP=24.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,740B, BPFP=1.7646 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,892B, BPFP=1.8236 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 49,784B, BPFP=0.7596 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 49,784B, BPFP=0.7596 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 39,744B, BPFP=1.2129 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.47578295 + text_encoder-item0.clip_prompt_embeds 0.00021654 167.91788420 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.52663388 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.07825245 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00120224 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00577698 0.56894982 + vae.encoder_f1 0.00578348 0.56913251 + vae.decoder 0.00017559 0.02138683 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 6.24261552 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 337988 +BPFP 1.1959 bits/point +EBPFP 2.3918 equivalent bits/point +MSE 6.242616 +---------------------- -------------------------------------------------------- +Time: 0.775s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.2426 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,308B, BPFP=24.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,720B, BPFP=1.7208 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,556B, BPFP=22.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,272B, BPFP=1.8078 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 73,224B, BPFP=1.8573 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 56,772B, BPFP=0.8663 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 56,772B, BPFP=0.8663 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 32,256B, BPFP=0.9844 +⌛️ [2/4] FRONTEND: Frontend time: 0.304s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.51773858 + text_encoder-item0.clip_prompt_embeds 0.00022160 96.20558543 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.48401198 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.07685303 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00135345 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00668450 0.70928884 + vae.encoder_f1 0.00668875 0.70954418 + vae.decoder 0.00023059 0.02081185 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 4.43197330 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 346944 +BPFP 1.2276 bits/point +EBPFP 2.4552 equivalent bits/point +MSE 4.431973 +---------------------- -------------------------------------------------------- +Time: 0.777s Load: 0.009s, Pack+Encode: 0.304s, Decode+Unpack: 0.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4320 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,380B, BPFP=24.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,428B, BPFP=1.8166 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,492B, BPFP=21.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,960B, BPFP=1.8636 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 73,776B, BPFP=1.8713 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 48,044B, BPFP=0.7331 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 48,036B, BPFP=0.7330 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,232B, BPFP=0.7090 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.47330427 + text_encoder-item0.clip_prompt_embeds 0.00023190 48.67861793 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.46719513 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.08613259 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00132737 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.04018118 1.37007689 + vae.encoder_f1 0.04018488 1.36999381 + vae.decoder 0.00016201 0.01441940 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 3.49491969 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 322412 +BPFP 1.1408 bits/point +EBPFP 2.2816 equivalent bits/point +MSE 3.494920 +---------------------- -------------------------------------------------------- +Time: 0.770s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4949 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,420B, BPFP=25.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,144B, BPFP=1.6429 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,724B, BPFP=23.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,564B, BPFP=1.7503 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,924B, BPFP=1.8244 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 57,508B, BPFP=0.8775 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 57,500B, BPFP=0.8774 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,288B, BPFP=1.0159 +⌛️ [2/4] FRONTEND: Frontend time: 0.302s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.49609264 + text_encoder-item0.clip_prompt_embeds 0.00023140 84.40095712 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.46329479 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.07499158 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00129849 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.04874706 1.19500160 + vae.encoder_f1 0.04875064 1.19441509 + vae.decoder 0.00019641 0.01771322 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 4.34782026 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 347136 +BPFP 1.2283 bits/point +EBPFP 2.4565 equivalent bits/point +MSE 4.347820 +---------------------- -------------------------------------------------------- +Time: 0.775s Load: 0.008s, Pack+Encode: 0.302s, Decode+Unpack: 0.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.3478 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,372B, BPFP=24.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,600B, BPFP=1.7045 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,668B, BPFP=22.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,708B, BPFP=1.7620 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,912B, BPFP=1.7226 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 68,468B, BPFP=1.0447 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 68,472B, BPFP=1.0448 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,252B, BPFP=0.7706 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.48138889 + text_encoder-item0.clip_prompt_embeds 0.00030893 23.71936934 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.52510552 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.08044514 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00121198 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.01360236 1.18262327 + vae.encoder_f1 0.01360807 1.18224573 + vae.decoder 0.00023006 0.01823518 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 2.75532388 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 357516 +BPFP 1.2650 bits/point +EBPFP 2.5300 equivalent bits/point +MSE 2.755324 +---------------------- -------------------------------------------------------- +Time: 0.769s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7553 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,472B, BPFP=25.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 14,020B, BPFP=1.8966 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,700B, BPFP=23.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,404B, BPFP=1.8185 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 73,548B, BPFP=1.8656 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,328B, BPFP=0.3712 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,328B, BPFP=0.3712 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,184B, BPFP=0.4023 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.54265690 + text_encoder-item0.clip_prompt_embeds 0.00024198 155.83172687 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.50633712 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.11615043 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00119178 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 1.67190456 4.71110821 + vae.encoder_f1 1.67190480 4.71055174 + vae.decoder 0.00017417 0.00939602 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 7.84760454 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 265048 +BPFP 0.9378 bits/point +EBPFP 1.8756 equivalent bits/point +MSE 7.847605 +---------------------- -------------------------------------------------------- +Time: 0.763s Load: 0.007s, Pack+Encode: 0.298s, Decode+Unpack: 0.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.8476 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,364B, BPFP=24.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,968B, BPFP=1.7543 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,548B, BPFP=22.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,980B, BPFP=1.7841 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 74,220B, BPFP=1.8826 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 69,020B, BPFP=1.0532 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 69,024B, BPFP=1.0532 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,944B, BPFP=1.1580 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.48747981 + text_encoder-item0.clip_prompt_embeds 0.00025129 23.55641234 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.51996045 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.08000228 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00160508 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00621760 0.76899570 + vae.encoder_f1 0.00622505 0.76852208 + vae.decoder 0.00025114 0.02307880 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 2.55980847 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 378132 +BPFP 1.3379 bits/point +EBPFP 2.6759 equivalent bits/point +MSE 2.559808 +---------------------- -------------------------------------------------------- +Time: 0.763s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5598 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,236B, BPFP=23.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,372B, BPFP=1.6737 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,684B, BPFP=23.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,736B, BPFP=1.7643 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,492B, BPFP=1.7120 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 72,372B, BPFP=1.1043 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 72,372B, BPFP=1.1043 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 38,556B, BPFP=1.1766 +⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.51888049 + text_encoder-item0.clip_prompt_embeds 0.00020838 23.65058932 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.52990861 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.07540971 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00111033 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00675961 1.00314224 + vae.encoder_f1 0.00676652 1.00374055 + vae.decoder 0.00021373 0.02320858 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 2.67087205 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 377884 +BPFP 1.3371 bits/point +EBPFP 2.6741 equivalent bits/point +MSE 2.670872 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.010s, Pack+Encode: 0.290s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6709 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,340B, BPFP=24.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,712B, BPFP=1.7197 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,688B, BPFP=23.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,380B, BPFP=1.8166 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 75,816B, BPFP=1.9231 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,064B, BPFP=0.6571 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,060B, BPFP=0.6570 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 53,836B, BPFP=1.6429 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.50930520 + text_encoder-item0.clip_prompt_embeds 0.00021387 35.82431598 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.50193815 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.08109919 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00135092 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00596338 0.47066346 + vae.encoder_f1 0.00596322 0.47061217 + vae.decoder 0.00018207 0.02888557 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 2.74309793 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 343960 +BPFP 1.2170 bits/point +EBPFP 2.4340 equivalent bits/point +MSE 2.743098 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7431 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,356B, BPFP=24.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,584B, BPFP=1.7024 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,704B, BPFP=23.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,100B, BPFP=1.7938 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,452B, BPFP=1.7363 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,736B, BPFP=0.4995 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,736B, BPFP=0.4995 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 44,412B, BPFP=1.3553 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.47191763 + text_encoder-item0.clip_prompt_embeds 0.00022138 47.04744572 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.50264111 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.08334400 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00117561 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00552804 0.37992704 + vae.encoder_f1 0.00552758 0.37994546 + vae.decoder 0.00018040 0.02411232 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 2.99408085 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 306144 +BPFP 1.0832 bits/point +EBPFP 2.1664 equivalent bits/point +MSE 2.994081 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9941 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,440B, BPFP=25.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,320B, BPFP=1.6667 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,756B, BPFP=23.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,656B, BPFP=1.7578 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,668B, BPFP=1.7164 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,488B, BPFP=0.5873 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,484B, BPFP=0.5872 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,212B, BPFP=0.8304 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.49007730 + text_encoder-item0.clip_prompt_embeds 0.00024507 23.73426508 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.49623203 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.08659878 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00126268 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00721525 0.64476132 + vae.encoder_f1 0.00721777 0.64436603 + vae.decoder 0.00018707 0.01661482 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 2.50634020 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 299088 +BPFP 1.0583 bits/point +EBPFP 2.1165 equivalent bits/point +MSE 2.506340 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5063 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,424B, BPFP=25.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,740B, BPFP=1.5882 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,928B, BPFP=24.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,744B, BPFP=1.6838 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 62,544B, BPFP=1.5864 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 54,692B, BPFP=0.8345 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 54,696B, BPFP=0.8346 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,708B, BPFP=1.0287 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.50160547 + text_encoder-item0.clip_prompt_embeds 0.00046272 191.51636905 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.51882677 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.07738749 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00106671 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.01999603 1.20153999 + vae.encoder_f1 0.01999529 1.20093882 + vae.decoder 0.00024882 0.02237918 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 7.15308751 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 331540 +BPFP 1.1731 bits/point +EBPFP 2.3462 equivalent bits/point +MSE 7.153088 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.1531 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,392B, BPFP=24.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,032B, BPFP=1.6277 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,720B, BPFP=23.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,256B, BPFP=1.7253 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 64,552B, BPFP=1.6374 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 57,160B, BPFP=0.8722 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 57,160B, BPFP=0.8722 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,224B, BPFP=0.6782 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.51519823 + text_encoder-item0.clip_prompt_embeds 0.00020334 23.75412608 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.47212248 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.07956481 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00110097 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.01341345 1.09189987 + vae.encoder_f1 0.01341645 1.09189379 + vae.decoder 0.00018350 0.01396219 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 2.71367667 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 327560 +BPFP 1.1590 bits/point +EBPFP 2.3180 equivalent bits/point +MSE 2.713677 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7137 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.010s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,308B, BPFP=24.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,184B, BPFP=1.6483 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,716B, BPFP=23.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,440B, BPFP=1.7403 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 72,436B, BPFP=1.8374 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 62,848B, BPFP=0.9590 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 62,856B, BPFP=0.9591 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 44,540B, BPFP=1.3593 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.53660615 + text_encoder-item0.clip_prompt_embeds 0.00022316 96.48066322 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.51479607 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.07850448 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00142578 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00606298 0.65119195 + vae.encoder_f1 0.00607096 0.65096605 + vae.decoder 0.00023408 0.02561883 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 4.41277610 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 369392 +BPFP 1.3070 bits/point +EBPFP 2.6140 equivalent bits/point +MSE 4.412776 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.010s, Pack+Encode: 0.292s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4128 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,328B, BPFP=24.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,988B, BPFP=1.6218 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,776B, BPFP=23.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,372B, BPFP=1.7347 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,516B, BPFP=1.7887 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 58,076B, BPFP=0.8862 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 58,072B, BPFP=0.8861 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 40,436B, BPFP=1.2340 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.46075769 + text_encoder-item0.clip_prompt_embeds 0.00023597 47.82645935 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.50234394 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.07672961 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00129005 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00653100 0.70934260 + vae.encoder_f1 0.00653745 0.70936871 + vae.decoder 0.00020026 0.02215014 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 3.16672626 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 353628 +BPFP 1.2512 bits/point +EBPFP 2.5025 equivalent bits/point +MSE 3.166726 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.457s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1667 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,288B, BPFP=23.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,836B, BPFP=1.7365 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,804B, BPFP=23.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,092B, BPFP=1.7932 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,232B, BPFP=1.7307 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 58,400B, BPFP=0.8911 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 58,396B, BPFP=0.8911 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 28,344B, BPFP=0.8650 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.50802243 + text_encoder-item0.clip_prompt_embeds 0.00022433 120.48906757 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.52761889 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.07224874 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00123930 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00869686 1.19688261 + vae.encoder_f1 0.00870063 1.19614518 + vae.decoder 0.00021246 0.01918346 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 5.29262133 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 341456 +BPFP 1.2082 bits/point +EBPFP 2.4163 equivalent bits/point +MSE 5.292621 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.2926 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,324B, BPFP=24.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,076B, BPFP=1.7689 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,732B, BPFP=23.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,000B, BPFP=1.8669 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,536B, BPFP=1.7638 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 67,232B, BPFP=1.0259 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 67,236B, BPFP=1.0259 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,516B, BPFP=1.0228 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.52541637 + text_encoder-item0.clip_prompt_embeds 0.00022433 34.64081735 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.45581689 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.07613430 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00119501 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00626512 0.89477164 + vae.encoder_f1 0.00626949 0.89477986 + vae.decoder 0.00018936 0.01889040 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 2.90742762 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 366716 +BPFP 1.2975 bits/point +EBPFP 2.5951 equivalent bits/point +MSE 2.907428 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9074 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,304B, BPFP=24.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,560B, BPFP=1.8344 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,532B, BPFP=22.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,164B, BPFP=1.8802 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 72,720B, BPFP=1.8446 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 48,756B, BPFP=0.7440 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 48,756B, BPFP=0.7440 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,052B, BPFP=0.7340 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.460s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.56566926 + text_encoder-item0.clip_prompt_embeds 0.00026137 120.52751285 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.49686694 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.08122905 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00130268 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.35915655 2.30982113 + vae.encoder_f1 0.35915723 2.30903935 + vae.decoder 0.00024181 0.01732877 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 5.80994942 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 323908 +BPFP 1.1461 bits/point +EBPFP 2.2921 equivalent bits/point +MSE 5.809949 +---------------------- -------------------------------------------------------- +Time: 0.763s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.460s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.8099 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,344B, BPFP=24.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,288B, BPFP=1.5271 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,772B, BPFP=23.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,636B, BPFP=1.6750 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 65,492B, BPFP=1.6612 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,832B, BPFP=0.3484 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,832B, BPFP=0.3484 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,712B, BPFP=0.9373 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.52745442 + text_encoder-item0.clip_prompt_embeds 0.00021656 155.64517384 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.51618619 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.06933978 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00117382 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.29031765 1.97527528 + vae.encoder_f1 0.29031771 1.97528768 + vae.decoder 0.00019965 0.02115830 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 6.57338620 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 266972 +BPFP 0.9446 bits/point +EBPFP 1.8892 equivalent bits/point +MSE 6.573386 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.5734 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,316B, BPFP=24.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,388B, BPFP=1.5406 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,712B, BPFP=23.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,132B, BPFP=1.7153 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 62,304B, BPFP=1.5804 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 50,508B, BPFP=0.7707 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 50,512B, BPFP=0.7708 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 48,028B, BPFP=1.4657 +⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.53749744 + text_encoder-item0.clip_prompt_embeds 0.00025451 83.97307055 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.48756971 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.07394200 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00123060 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00595764 0.49202594 + vae.encoder_f1 0.00596395 0.49205506 + vae.decoder 0.00019845 0.02712763 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 4.01181810 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 336964 +BPFP 1.1923 bits/point +EBPFP 2.3845 equivalent bits/point +MSE 4.011818 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0118 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,244B, BPFP=23.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,280B, BPFP=1.7965 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,660B, BPFP=22.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,168B, BPFP=1.8805 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 72,656B, BPFP=1.8429 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,900B, BPFP=0.5783 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,896B, BPFP=0.5782 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,172B, BPFP=0.7377 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.50406118 + text_encoder-item0.clip_prompt_embeds 0.00026157 144.25101461 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.51753731 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.08685431 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00135130 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.40456498 3.15106440 + vae.encoder_f1 0.40456539 3.14994621 + vae.decoder 0.00020503 0.01713419 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 6.82071904 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 302040 +BPFP 1.0687 bits/point +EBPFP 2.1374 equivalent bits/point +MSE 6.820719 +---------------------- -------------------------------------------------------- +Time: 0.753s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.8207 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,336B, BPFP=24.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,888B, BPFP=1.7435 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,856B, BPFP=24.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,140B, BPFP=1.7159 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 65,324B, BPFP=1.6570 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 58,632B, BPFP=0.8947 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 58,632B, BPFP=0.8947 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 43,260B, BPFP=1.3202 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.48591455 + text_encoder-item0.clip_prompt_embeds 0.00027179 71.98405371 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.54521971 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.07653546 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00103451 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00673531 1.04844451 + vae.encoder_f1 0.00673732 1.04887092 + vae.decoder 0.00020129 0.02396178 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 3.95612174 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 353132 +BPFP 1.2495 bits/point +EBPFP 2.4990 equivalent bits/point +MSE 3.956122 +---------------------- -------------------------------------------------------- +Time: 0.758s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9561 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,360B, BPFP=24.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,056B, BPFP=1.7662 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,740B, BPFP=23.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,068B, BPFP=1.7912 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 73,144B, BPFP=1.8553 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 48,764B, BPFP=0.7441 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 48,768B, BPFP=0.7441 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,248B, BPFP=0.6790 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.48081660 + text_encoder-item0.clip_prompt_embeds 0.00023057 83.33961546 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.53325825 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.08637823 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00127551 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00881784 1.07970083 + vae.encoder_f1 0.00882136 1.07958722 + vae.decoder 0.00017598 0.01474020 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 4.26688069 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 321212 +BPFP 1.1365 bits/point +EBPFP 2.2731 equivalent bits/point +MSE 4.266881 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2669 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,388B, BPFP=24.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,732B, BPFP=1.7224 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,596B, BPFP=22.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,732B, BPFP=1.7640 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,472B, BPFP=1.7875 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 54,088B, BPFP=0.8253 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 54,092B, BPFP=0.8254 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 47,160B, BPFP=1.4392 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.45848346 + text_encoder-item0.clip_prompt_embeds 0.00025208 23.95837984 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.49840498 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.08663124 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00126138 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00582247 0.52246672 + vae.encoder_f1 0.00582996 0.52246433 + vae.decoder 0.00016099 0.02362412 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 2.45638107 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 353324 +BPFP 1.2502 bits/point +EBPFP 2.5003 equivalent bits/point +MSE 2.456381 +---------------------- -------------------------------------------------------- +Time: 0.766s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4564 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,420B, BPFP=25.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,392B, BPFP=1.6764 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,704B, BPFP=23.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,292B, BPFP=1.8094 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,252B, BPFP=1.7312 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 66,020B, BPFP=1.0074 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 66,016B, BPFP=1.0073 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 36,560B, BPFP=1.1157 +⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.466s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.49308046 + text_encoder-item0.clip_prompt_embeds 0.00020809 83.88460498 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.48315067 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.09029607 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00111407 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00602745 0.89384651 + vae.encoder_f1 0.00603159 0.89379698 + vae.decoder 0.00017526 0.02093792 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 4.19579903 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 364720 +BPFP 1.2905 bits/point +EBPFP 2.5810 equivalent bits/point +MSE 4.195799 +---------------------- -------------------------------------------------------- +Time: 0.774s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.466s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1958 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,376B, BPFP=24.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,000B, BPFP=1.6234 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,648B, BPFP=22.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,316B, BPFP=1.7302 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 66,076B, BPFP=1.6760 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 67,436B, BPFP=1.0290 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 67,436B, BPFP=1.0290 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 40,012B, BPFP=1.2211 +⌛️ [2/4] FRONTEND: Frontend time: 0.301s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.52492702 + text_encoder-item0.clip_prompt_embeds 0.00020908 23.71116790 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.51482992 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.07916478 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00124098 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00634616 0.86809397 + vae.encoder_f1 0.00635208 0.86802471 + vae.decoder 0.00022721 0.02233287 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 2.60974450 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 367364 +BPFP 1.2998 bits/point +EBPFP 2.5997 equivalent bits/point +MSE 2.609744 +---------------------- -------------------------------------------------------- +Time: 0.777s Load: 0.009s, Pack+Encode: 0.301s, Decode+Unpack: 0.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6097 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,348B, BPFP=24.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,160B, BPFP=1.6450 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,788B, BPFP=23.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,920B, BPFP=1.6981 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 65,004B, BPFP=1.6488 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,236B, BPFP=0.5071 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,236B, BPFP=0.5071 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,928B, BPFP=0.5776 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.466s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.49475741 + text_encoder-item0.clip_prompt_embeds 0.00022947 132.49727746 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.55463305 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.07349422 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00110960 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.05448642 1.12797141 + vae.encoder_f1 0.05448771 1.12816596 + vae.decoder 0.00017748 0.01409720 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 5.57440932 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 276684 +BPFP 0.9790 bits/point +EBPFP 1.9580 equivalent bits/point +MSE 5.574409 +---------------------- -------------------------------------------------------- +Time: 0.772s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.466s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.5744 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,356B, BPFP=24.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,436B, BPFP=1.6824 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,796B, BPFP=23.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,356B, BPFP=1.8146 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,736B, BPFP=1.7689 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 46,060B, BPFP=0.7028 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 46,056B, BPFP=0.7028 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,840B, BPFP=0.5750 +⌛️ [2/4] FRONTEND: Frontend time: 0.301s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.50963799 + text_encoder-item0.clip_prompt_embeds 0.00020169 36.63345086 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.49709654 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.07620022 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00137747 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.06876971 1.70475209 + vae.encoder_f1 0.06877109 1.70504105 + vae.decoder 0.00023999 0.01337044 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 3.33465928 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 308700 +BPFP 1.0923 bits/point +EBPFP 2.1845 equivalent bits/point +MSE 3.334659 +---------------------- -------------------------------------------------------- +Time: 0.776s Load: 0.009s, Pack+Encode: 0.301s, Decode+Unpack: 0.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3347 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,380B, BPFP=24.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,620B, BPFP=1.7073 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,720B, BPFP=23.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,212B, BPFP=1.7218 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,256B, BPFP=1.7313 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 50,992B, BPFP=0.7781 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 50,992B, BPFP=0.7781 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 47,532B, BPFP=1.4506 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.47360365 + text_encoder-item0.clip_prompt_embeds 0.00025253 23.73753297 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.54306488 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.11932257 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00119023 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00595097 0.49281138 + vae.encoder_f1 0.00595882 0.49284381 + vae.decoder 0.00020134 0.02782257 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 2.43879205 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 344768 +BPFP 1.2199 bits/point +EBPFP 2.4398 equivalent bits/point +MSE 2.438792 +---------------------- -------------------------------------------------------- +Time: 0.769s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4388 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,428B, BPFP=25.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,652B, BPFP=1.7116 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,844B, BPFP=24.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,192B, BPFP=1.8013 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,540B, BPFP=1.7385 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,448B, BPFP=0.6172 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,448B, BPFP=0.6172 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,216B, BPFP=1.0137 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.50924591 + text_encoder-item0.clip_prompt_embeds 0.00022201 59.93744927 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.52193484 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.12586998 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00134491 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00831743 0.84119773 + vae.encoder_f1 0.00831926 0.84146702 + vae.decoder 0.00028593 0.02029091 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 3.54665623 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 310832 +BPFP 1.0998 bits/point +EBPFP 2.1996 equivalent bits/point +MSE 3.546656 +---------------------- -------------------------------------------------------- +Time: 0.773s Load: 0.008s, Pack+Encode: 0.300s, Decode+Unpack: 0.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5467 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,288B, BPFP=23.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,084B, BPFP=1.7700 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,732B, BPFP=23.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,280B, BPFP=1.8084 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 72,876B, BPFP=1.8485 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 66,868B, BPFP=1.0203 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 66,868B, BPFP=1.0203 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 35,428B, BPFP=1.0812 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.54362551 + text_encoder-item0.clip_prompt_embeds 0.00026808 35.81664299 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.53691812 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.08287752 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00129219 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00606586 0.90239537 + vae.encoder_f1 0.00607066 0.90251613 + vae.decoder 0.00019664 0.02154293 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 2.94241010 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 370488 +BPFP 1.3109 bits/point +EBPFP 2.6218 equivalent bits/point +MSE 2.942410 +---------------------- -------------------------------------------------------- +Time: 0.770s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9424 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,252B, BPFP=23.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,912B, BPFP=1.7468 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,780B, BPFP=23.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,560B, BPFP=1.9123 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 73,376B, BPFP=1.8612 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 49,744B, BPFP=0.7590 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 49,740B, BPFP=0.7590 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 34,344B, BPFP=1.0481 +⌛️ [2/4] FRONTEND: Frontend time: 0.304s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.468s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.52347287 + text_encoder-item0.clip_prompt_embeds 0.00023198 48.14593479 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.54044819 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.08290307 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00117175 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.05216765 1.61536670 + vae.encoder_f1 0.05216896 1.61537707 + vae.decoder 0.00017960 0.01899431 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 3.59519313 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 336772 +BPFP 1.1916 bits/point +EBPFP 2.3832 equivalent bits/point +MSE 3.595193 +---------------------- -------------------------------------------------------- +Time: 0.780s Load: 0.007s, Pack+Encode: 0.304s, Decode+Unpack: 0.468s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5952 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,312B, BPFP=24.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,824B, BPFP=1.7348 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,792B, BPFP=23.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,324B, BPFP=1.7308 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 66,916B, BPFP=1.6973 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 67,904B, BPFP=1.0361 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 67,904B, BPFP=1.0361 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,464B, BPFP=1.1433 +⌛️ [2/4] FRONTEND: Frontend time: 0.303s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.50486636 + text_encoder-item0.clip_prompt_embeds 0.00023125 190.75740666 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.56392651 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.08125670 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00123059 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00620361 0.78358567 + vae.encoder_f1 0.00620966 0.78373992 + vae.decoder 0.00020748 0.02170954 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 6.93971874 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 367504 +BPFP 1.3003 bits/point +EBPFP 2.6007 equivalent bits/point +MSE 6.939719 +---------------------- -------------------------------------------------------- +Time: 0.776s Load: 0.009s, Pack+Encode: 0.303s, Decode+Unpack: 0.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.9397 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,440B, BPFP=25.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,912B, BPFP=1.7468 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,656B, BPFP=22.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,052B, BPFP=1.7899 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,620B, BPFP=1.7659 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 57,636B, BPFP=0.8795 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 57,632B, BPFP=0.8794 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,536B, BPFP=1.1455 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.45993360 + text_encoder-item0.clip_prompt_embeds 0.00023066 36.41133996 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.48206329 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.08382292 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00131729 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.03159856 1.00012004 + vae.encoder_f1 0.03160188 1.00028515 + vae.decoder 0.00018417 0.02025001 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 3.00313153 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 350548 +BPFP 1.2403 bits/point +EBPFP 2.4807 equivalent bits/point +MSE 3.003132 +---------------------- -------------------------------------------------------- +Time: 0.771s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0031 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,352B, BPFP=24.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,912B, BPFP=1.7468 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,692B, BPFP=23.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,404B, BPFP=1.8185 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,940B, BPFP=1.8248 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 70,420B, BPFP=1.0745 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 70,416B, BPFP=1.0745 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 35,856B, BPFP=1.0942 +⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.47190841 + text_encoder-item0.clip_prompt_embeds 0.00024948 47.83052202 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.44361448 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.07657974 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00134123 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.03490865 1.76708245 + vae.encoder_f1 0.03491008 1.76675832 + vae.decoder 0.00028462 0.02449891 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 3.65754079 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 377056 +BPFP 1.3341 bits/point +EBPFP 2.6683 equivalent bits/point +MSE 3.657541 +---------------------- -------------------------------------------------------- +Time: 0.773s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6575 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,324B, BPFP=24.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,988B, BPFP=1.6218 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,564B, BPFP=22.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,488B, BPFP=1.8253 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,844B, BPFP=1.8223 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,080B, BPFP=0.5658 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,084B, BPFP=0.5659 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 46,332B, BPFP=1.4139 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.49246740 + text_encoder-item0.clip_prompt_embeds 0.00021560 23.74827093 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.49839144 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.07869168 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00122574 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00544735 0.40402344 + vae.encoder_f1 0.00544843 0.40396836 + vae.decoder 0.00018632 0.02519291 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 2.39578562 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 319768 +BPFP 1.1314 bits/point +EBPFP 2.2629 equivalent bits/point +MSE 2.395786 +---------------------- -------------------------------------------------------- +Time: 0.770s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3958 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,232B, BPFP=23.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,008B, BPFP=1.6245 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,804B, BPFP=23.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,840B, BPFP=1.7727 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,520B, BPFP=1.7127 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 60,616B, BPFP=0.9249 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 60,612B, BPFP=0.9249 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 36,756B, BPFP=1.1217 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.50398537 + text_encoder-item0.clip_prompt_embeds 0.00022698 180.14133523 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.52750278 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.07842491 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00123545 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00630479 0.71739650 + vae.encoder_f1 0.00631430 0.71754038 + vae.decoder 0.00018596 0.02009618 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 6.63102697 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 352452 +BPFP 1.2471 bits/point +EBPFP 2.4941 equivalent bits/point +MSE 6.631027 +---------------------- -------------------------------------------------------- +Time: 0.773s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.6310 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,404B, BPFP=25.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,184B, BPFP=1.6483 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,780B, BPFP=23.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,412B, BPFP=1.7380 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 65,404B, BPFP=1.6590 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 58,328B, BPFP=0.8900 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 58,340B, BPFP=0.8902 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 42,136B, BPFP=1.2859 +⌛️ [2/4] FRONTEND: Frontend time: 0.304s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.52021043 + text_encoder-item0.clip_prompt_embeds 0.00024643 144.01396780 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.53653822 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.07997934 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00110564 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00612578 0.62046170 + vae.encoder_f1 0.00613243 0.62047559 + vae.decoder 0.00018179 0.02162029 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 5.64137118 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 351052 +BPFP 1.2421 bits/point +EBPFP 2.4842 equivalent bits/point +MSE 5.641371 +---------------------- -------------------------------------------------------- +Time: 0.777s Load: 0.009s, Pack+Encode: 0.304s, Decode+Unpack: 0.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6414 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,316B, BPFP=24.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 14,048B, BPFP=1.9004 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,788B, BPFP=23.6750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 24,908B, BPFP=2.0218 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 77,220B, BPFP=1.9587 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,116B, BPFP=0.2764 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,116B, BPFP=0.2764 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 46,724B, BPFP=1.4259 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.48118234 + text_encoder-item0.clip_prompt_embeds 0.00024049 23.80690273 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.52865558 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.08980509 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00142007 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00526071 0.21915515 + vae.encoder_f1 0.00526072 0.21916495 + vae.decoder 0.00016981 0.02302129 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 2.31187123 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 292300 +BPFP 1.0342 bits/point +EBPFP 2.0685 equivalent bits/point +MSE 2.311871 +---------------------- -------------------------------------------------------- +Time: 0.770s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.3119 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,312B, BPFP=24.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,380B, BPFP=1.6748 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,660B, BPFP=22.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,908B, BPFP=1.6971 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,920B, BPFP=1.7228 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 62,080B, BPFP=0.9473 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 62,092B, BPFP=0.9474 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 40,384B, BPFP=1.2324 +⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.48976898 + text_encoder-item0.clip_prompt_embeds 0.00022843 60.66954140 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.50823560 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.07512865 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00129153 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00622977 0.72328627 + vae.encoder_f1 0.00623684 0.72343653 + vae.decoder 0.00019755 0.02191581 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 3.50904753 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 358800 +BPFP 1.2695 bits/point +EBPFP 2.5391 equivalent bits/point +MSE 3.509048 +---------------------- -------------------------------------------------------- +Time: 0.772s Load: 0.008s, Pack+Encode: 0.299s, Decode+Unpack: 0.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5090 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,408B, BPFP=25.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,196B, BPFP=1.7852 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,800B, BPFP=23.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,796B, BPFP=1.7692 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,064B, BPFP=1.7265 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 44,664B, BPFP=0.6815 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 44,664B, BPFP=0.6815 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,400B, BPFP=0.9277 +⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.50971278 + text_encoder-item0.clip_prompt_embeds 0.00026004 47.90287219 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.55880423 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.07980881 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00120938 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00725303 0.70728201 + vae.encoder_f1 0.00725507 0.70711422 + vae.decoder 0.00017991 0.01756849 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 3.16736459 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 316056 +BPFP 1.1183 bits/point +EBPFP 2.2366 equivalent bits/point +MSE 3.167365 +---------------------- -------------------------------------------------------- +Time: 0.772s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1674 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,340B, BPFP=24.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,368B, BPFP=1.8084 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,820B, BPFP=23.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,232B, BPFP=1.8045 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,064B, BPFP=1.7518 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 44,112B, BPFP=0.6731 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 44,116B, BPFP=0.6732 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,584B, BPFP=0.7502 +⌛️ [2/4] FRONTEND: Frontend time: 0.301s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.53437507 + text_encoder-item0.clip_prompt_embeds 0.00031748 23.76459771 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.58040566 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.08562690 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00119875 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.42111695 2.52626872 + vae.encoder_f1 0.42111716 2.52713490 + vae.decoder 0.00019827 0.01641201 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 3.37999710 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 310700 +BPFP 1.0993 bits/point +EBPFP 2.1987 equivalent bits/point +MSE 3.379997 +---------------------- -------------------------------------------------------- +Time: 0.773s Load: 0.008s, Pack+Encode: 0.301s, Decode+Unpack: 0.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3800 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,384B, BPFP=24.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,924B, BPFP=1.7484 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,640B, BPFP=22.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,384B, BPFP=1.8169 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 73,932B, BPFP=1.8753 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 53,672B, BPFP=0.8190 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 53,672B, BPFP=0.8190 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 28,840B, BPFP=0.8801 +⌛️ [2/4] FRONTEND: Frontend time: 0.301s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.466s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.49854306 + text_encoder-item0.clip_prompt_embeds 0.00024951 119.91502638 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.49751949 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.08333195 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00136960 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.10376993 2.41922903 + vae.encoder_f1 0.10377157 2.41908550 + vae.decoder 0.00019787 0.01805470 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 5.84498054 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 338512 +BPFP 1.1977 bits/point +EBPFP 2.3955 equivalent bits/point +MSE 5.844981 +---------------------- -------------------------------------------------------- +Time: 0.775s Load: 0.009s, Pack+Encode: 0.301s, Decode+Unpack: 0.466s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.8450 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,320B, BPFP=24.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,796B, BPFP=1.7311 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,660B, BPFP=22.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,916B, BPFP=1.7789 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,736B, BPFP=1.7181 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 55,208B, BPFP=0.8424 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 55,208B, BPFP=0.8424 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,576B, BPFP=0.8110 +⌛️ [2/4] FRONTEND: Frontend time: 0.304s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.48565257 + text_encoder-item0.clip_prompt_embeds 0.00022350 108.41731771 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.49732246 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.08280684 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00104349 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.01346414 1.14341223 + vae.encoder_f1 0.01346933 1.14482534 + vae.decoder 0.00019243 0.01649771 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 4.95268363 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 332484 +BPFP 1.1764 bits/point +EBPFP 2.3528 equivalent bits/point +MSE 4.952684 +---------------------- -------------------------------------------------------- +Time: 0.774s Load: 0.008s, Pack+Encode: 0.304s, Decode+Unpack: 0.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.9527 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,448B, BPFP=25.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,748B, BPFP=1.7246 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,776B, BPFP=23.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,736B, BPFP=1.7643 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 73,364B, BPFP=1.8609 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 47,504B, BPFP=0.7249 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 47,500B, BPFP=0.7248 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,004B, BPFP=0.8241 +⌛️ [2/4] FRONTEND: Frontend time: 0.302s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.49797034 + text_encoder-item0.clip_prompt_embeds 0.00024958 119.63567877 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.55335102 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.08308481 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00127722 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.11196710 1.84779167 + vae.encoder_f1 0.11196851 1.84796393 + vae.decoder 0.00023459 0.01975918 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 5.57293840 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 323144 +BPFP 1.1434 bits/point +EBPFP 2.2867 equivalent bits/point +MSE 5.572938 +---------------------- -------------------------------------------------------- +Time: 0.780s Load: 0.008s, Pack+Encode: 0.302s, Decode+Unpack: 0.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.5729 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,408B, BPFP=25.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,588B, BPFP=1.8382 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,840B, BPFP=24.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,244B, BPFP=1.8055 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,296B, BPFP=1.7070 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 66,300B, BPFP=1.0117 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 66,304B, BPFP=1.0117 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 34,172B, BPFP=1.0428 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.47203640 + text_encoder-item0.clip_prompt_embeds 0.00025929 59.78582420 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.53630886 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.13297087 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00113334 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00675017 0.93124115 + vae.encoder_f1 0.00675421 0.93134141 + vae.decoder 0.00023635 0.02251898 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 3.58494442 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 363216 +BPFP 1.2852 bits/point +EBPFP 2.5703 equivalent bits/point +MSE 3.584944 +---------------------- -------------------------------------------------------- +Time: 0.780s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5849 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,344B, BPFP=24.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,176B, BPFP=1.6472 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,720B, BPFP=23.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,308B, BPFP=1.7295 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,088B, BPFP=1.8032 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 74,980B, BPFP=1.1441 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 74,980B, BPFP=1.1441 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,956B, BPFP=1.0363 +⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.45372311 + text_encoder-item0.clip_prompt_embeds 0.00064775 179.77473958 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.52098689 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.07480772 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00138885 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00728993 1.18077743 + vae.encoder_f1 0.00729572 1.18090463 + vae.decoder 0.00026488 0.02244144 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 6.83645101 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 381616 +BPFP 1.3503 bits/point +EBPFP 2.7005 equivalent bits/point +MSE 6.836451 +---------------------- -------------------------------------------------------- +Time: 0.772s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.8365 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,424B, BPFP=25.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,996B, BPFP=1.7581 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,776B, BPFP=23.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,752B, BPFP=1.7656 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,856B, BPFP=1.8226 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 65,648B, BPFP=1.0017 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 65,648B, BPFP=1.0017 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 35,988B, BPFP=1.0983 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.55332029 + text_encoder-item0.clip_prompt_embeds 0.00023188 23.77874729 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.51550674 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.08121444 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00122988 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00613207 0.74432629 + vae.encoder_f1 0.00613899 0.74413580 + vae.decoder 0.00023812 0.02258370 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 2.55421132 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 367152 +BPFP 1.2991 bits/point +EBPFP 2.5982 equivalent bits/point +MSE 2.554211 +---------------------- -------------------------------------------------------- +Time: 0.773s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.5542 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,412B, BPFP=25.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,996B, BPFP=1.7581 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,624B, BPFP=22.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,848B, BPFP=1.7734 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,020B, BPFP=1.8014 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 55,760B, BPFP=0.8508 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 55,760B, BPFP=0.8508 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 32,388B, BPFP=0.9884 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.46438134 + text_encoder-item0.clip_prompt_embeds 0.00023678 34.75587417 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.52131362 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.08598251 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00118111 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00636537 0.87504423 + vae.encoder_f1 0.00636991 0.87537432 + vae.decoder 0.00025538 0.02209177 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 2.90217750 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 342872 +BPFP 1.2132 bits/point +EBPFP 2.4263 equivalent bits/point +MSE 2.902177 +---------------------- -------------------------------------------------------- +Time: 0.767s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9022 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,384B, BPFP=24.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,124B, BPFP=1.6402 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,508B, BPFP=21.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,900B, BPFP=1.6964 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 66,024B, BPFP=1.6747 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 54,896B, BPFP=0.8376 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 54,900B, BPFP=0.8377 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,796B, BPFP=0.7872 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.50232073 + text_encoder-item0.clip_prompt_embeds 0.00023432 144.56659226 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.46344872 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.07839063 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00126104 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.23155926 2.37509537 + vae.encoder_f1 0.23156048 2.37418079 + vae.decoder 0.00018572 0.01659951 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 6.46867572 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 327596 +BPFP 1.1591 bits/point +EBPFP 2.3182 equivalent bits/point +MSE 6.468676 +---------------------- -------------------------------------------------------- +Time: 0.772s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.4687 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,328B, BPFP=24.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,116B, BPFP=1.6391 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,672B, BPFP=22.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,708B, BPFP=1.7620 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,328B, BPFP=1.7585 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 68,504B, BPFP=1.0453 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 68,500B, BPFP=1.0452 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 40,684B, BPFP=1.2416 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.52839712 + text_encoder-item0.clip_prompt_embeds 0.00022528 84.14475954 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.52532291 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.07652411 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00135395 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00729824 0.97324800 + vae.encoder_f1 0.00730369 0.97339004 + vae.decoder 0.00019938 0.02409129 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 4.23930625 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 373904 +BPFP 1.3230 bits/point +EBPFP 2.6459 equivalent bits/point +MSE 4.239306 +---------------------- -------------------------------------------------------- +Time: 0.769s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2393 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,328B, BPFP=24.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,688B, BPFP=1.5812 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,744B, BPFP=23.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,740B, BPFP=1.7646 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,928B, BPFP=1.6216 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 45,992B, BPFP=0.7018 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 45,988B, BPFP=0.7017 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 45,332B, BPFP=1.3834 +⌛️ [2/4] FRONTEND: Frontend time: 0.301s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.46040050 + text_encoder-item0.clip_prompt_embeds 0.00022149 143.95987216 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.51889801 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.08532460 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00107802 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00564371 0.54373801 + vae.encoder_f1 0.00565042 0.54394186 + vae.decoder 0.00019980 0.02516175 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 5.60502782 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 327804 +BPFP 1.1599 bits/point +EBPFP 2.3197 equivalent bits/point +MSE 5.605028 +---------------------- -------------------------------------------------------- +Time: 0.771s Load: 0.009s, Pack+Encode: 0.301s, Decode+Unpack: 0.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6050 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,424B, BPFP=25.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,532B, BPFP=1.6953 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,796B, BPFP=23.7250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,304B, BPFP=1.8104 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 66,656B, BPFP=1.6907 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 49,780B, BPFP=0.7596 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 49,788B, BPFP=0.7597 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 43,572B, BPFP=1.3297 +⌛️ [2/4] FRONTEND: Frontend time: 0.301s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.46923780 + text_encoder-item0.clip_prompt_embeds 0.00022173 71.74961107 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.50175037 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.09104679 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00100949 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00576096 0.49931312 + vae.encoder_f1 0.00576981 0.49893039 + vae.decoder 0.00019592 0.02383656 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 3.69571695 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 337916 +BPFP 1.1956 bits/point +EBPFP 2.3913 equivalent bits/point +MSE 3.695717 +---------------------- -------------------------------------------------------- +Time: 0.772s Load: 0.008s, Pack+Encode: 0.301s, Decode+Unpack: 0.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6957 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,480B, BPFP=25.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,776B, BPFP=1.8636 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,772B, BPFP=23.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,060B, BPFP=1.8718 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 74,668B, BPFP=1.8940 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,916B, BPFP=0.6396 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,916B, BPFP=0.6396 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,028B, BPFP=0.9164 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.50556572 + text_encoder-item0.clip_prompt_embeds 0.00025917 84.77468885 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.51933002 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.08904693 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00124197 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00594818 0.56097913 + vae.encoder_f1 0.00595328 0.56112051 + vae.decoder 0.00023462 0.01981568 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 4.06460806 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 318680 +BPFP 1.1276 bits/point +EBPFP 2.2552 equivalent bits/point +MSE 4.064608 +---------------------- -------------------------------------------------------- +Time: 0.771s Load: 0.008s, Pack+Encode: 0.300s, Decode+Unpack: 0.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0646 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,356B, BPFP=24.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 14,272B, BPFP=1.9307 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,856B, BPFP=24.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 24,184B, BPFP=1.9630 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 75,832B, BPFP=1.9235 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,720B, BPFP=0.5756 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,720B, BPFP=0.5756 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,712B, BPFP=0.4795 +⌛️ [2/4] FRONTEND: Frontend time: 0.304s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.466s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.47235640 + text_encoder-item0.clip_prompt_embeds 0.00022579 23.76673684 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.52824516 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.11955396 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00142868 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.85445058 3.82381535 + vae.encoder_f1 0.85445166 3.82573938 + vae.decoder 0.00025257 0.01197428 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 3.98300499 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 298716 +BPFP 1.0569 bits/point +EBPFP 2.1139 equivalent bits/point +MSE 3.983005 +---------------------- -------------------------------------------------------- +Time: 0.778s Load: 0.008s, Pack+Encode: 0.304s, Decode+Unpack: 0.466s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9830 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,404B, BPFP=25.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,448B, BPFP=1.6840 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,740B, BPFP=23.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,736B, BPFP=1.7643 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,920B, BPFP=1.7735 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 72,004B, BPFP=1.0987 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 72,004B, BPFP=1.0987 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 43,968B, BPFP=1.3418 +⌛️ [2/4] FRONTEND: Frontend time: 0.301s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.45654376 + text_encoder-item0.clip_prompt_embeds 0.00025458 58.80261178 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.49952922 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.12080444 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00120752 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00628510 0.77043593 + vae.encoder_f1 0.00629234 0.77047807 + vae.decoder 0.00023521 0.02617096 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 3.48451612 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 385288 +BPFP 1.3633 bits/point +EBPFP 2.7265 equivalent bits/point +MSE 3.484516 +---------------------- -------------------------------------------------------- +Time: 0.772s Load: 0.008s, Pack+Encode: 0.301s, Decode+Unpack: 0.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4845 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,316B, BPFP=24.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,348B, BPFP=1.6705 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,780B, BPFP=23.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,516B, BPFP=1.7464 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,464B, BPFP=1.8127 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 51,392B, BPFP=0.7842 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 51,392B, BPFP=0.7842 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 38,744B, BPFP=1.1824 +⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.52794874 + text_encoder-item0.clip_prompt_embeds 0.00022807 155.18114177 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.56390877 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.07938852 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00126110 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00573429 0.53626847 + vae.encoder_f1 0.00574192 0.53630054 + vae.decoder 0.00017875 0.02077804 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 5.89432189 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 340016 +BPFP 1.2031 bits/point +EBPFP 2.4061 equivalent bits/point +MSE 5.894322 +---------------------- -------------------------------------------------------- +Time: 0.771s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.8943 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,332B, BPFP=24.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 14,656B, BPFP=1.9827 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,660B, BPFP=22.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 25,108B, BPFP=2.0380 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 86,512B, BPFP=2.1944 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 65,092B, BPFP=0.9932 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 65,092B, BPFP=0.9932 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 40,056B, BPFP=1.2224 +⌛️ [2/4] FRONTEND: Frontend time: 0.302s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.466s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.50380373 + text_encoder-item0.clip_prompt_embeds 0.00027120 47.87293696 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.51744938 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.09034401 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00180148 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00781570 1.10295236 + vae.encoder_f1 0.00781878 1.10300326 + vae.decoder 0.00029724 0.02721874 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 3.35176693 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 389572 +BPFP 1.3784 bits/point +EBPFP 2.7568 equivalent bits/point +MSE 3.351767 +---------------------- -------------------------------------------------------- +Time: 0.775s Load: 0.008s, Pack+Encode: 0.302s, Decode+Unpack: 0.466s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3518 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,432B, BPFP=25.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,592B, BPFP=1.7035 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,504B, BPFP=21.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,208B, BPFP=1.8026 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,164B, BPFP=1.7797 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 59,456B, BPFP=0.9072 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 59,468B, BPFP=0.9074 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 46,992B, BPFP=1.4341 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.53115157 + text_encoder-item0.clip_prompt_embeds 0.00022930 60.60784040 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.48325181 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.08385413 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00121788 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00577752 0.54080039 + vae.encoder_f1 0.00578475 0.54072678 + vae.decoder 0.00024190 0.02594409 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 3.42358774 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 363880 +BPFP 1.2875 bits/point +EBPFP 2.5750 equivalent bits/point +MSE 3.423588 +---------------------- -------------------------------------------------------- +Time: 0.811s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4236 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,344B, BPFP=24.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 14,232B, BPFP=1.9253 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,940B, BPFP=24.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,580B, BPFP=1.9140 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 75,520B, BPFP=1.9156 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 62,396B, BPFP=0.9521 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 62,384B, BPFP=0.9519 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,092B, BPFP=0.6742 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.54648733 + text_encoder-item0.clip_prompt_embeds 0.00028764 35.77863738 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.55077477 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.07889577 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00146587 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.03343784 1.86558807 + vae.encoder_f1 0.03344063 1.86563993 + vae.decoder 0.00016139 0.01404665 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 3.38708849 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 353552 +BPFP 1.2510 bits/point +EBPFP 2.5019 equivalent bits/point +MSE 3.387088 +---------------------- -------------------------------------------------------- +Time: 0.764s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.457s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3871 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,300B, BPFP=23.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,980B, BPFP=1.6207 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,644B, BPFP=22.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,280B, BPFP=1.7273 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,608B, BPFP=1.7403 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 70,512B, BPFP=1.0759 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 70,516B, BPFP=1.0760 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 42,252B, BPFP=1.2894 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.51390346 + text_encoder-item0.clip_prompt_embeds 0.00023094 156.06772524 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.47902541 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.07915428 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00128839 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00637455 0.83273411 + vae.encoder_f1 0.00637988 0.83272392 + vae.decoder 0.00020059 0.02313625 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 6.05520608 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 378156 +BPFP 1.3380 bits/point +EBPFP 2.6760 equivalent bits/point +MSE 6.055206 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.0552 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.011s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,308B, BPFP=24.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,208B, BPFP=1.7868 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,852B, BPFP=24.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,220B, BPFP=1.8036 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,512B, BPFP=1.8139 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 58,180B, BPFP=0.8878 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 58,180B, BPFP=0.8878 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 41,032B, BPFP=1.2522 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.51919866 + text_encoder-item0.clip_prompt_embeds 0.00025217 24.07511288 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.59651647 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.07665325 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00121887 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00581597 0.55849028 + vae.encoder_f1 0.00582356 0.55836791 + vae.decoder 0.00019494 0.02260762 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 2.47563039 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 357556 +BPFP 1.2651 bits/point +EBPFP 2.5303 equivalent bits/point +MSE 2.475630 +---------------------- -------------------------------------------------------- +Time: 0.760s Load: 0.011s, Pack+Encode: 0.295s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4756 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,400B, BPFP=25.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,080B, BPFP=1.6342 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,808B, BPFP=23.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,112B, BPFP=1.6325 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 63,920B, BPFP=1.6213 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,256B, BPFP=0.5074 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,256B, BPFP=0.5074 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 33,512B, BPFP=1.0227 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.51617547 + text_encoder-item0.clip_prompt_embeds 0.00026975 23.58670269 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.54160380 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.06935856 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00111629 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 1.11695218 3.40792990 + vae.encoder_f1 1.11695278 3.40815735 + vae.decoder 0.00019720 0.02036643 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 3.78379218 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 289408 +BPFP 1.0240 bits/point +EBPFP 2.0480 equivalent bits/point +MSE 3.783792 +---------------------- -------------------------------------------------------- +Time: 0.761s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7838 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,408B, BPFP=25.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,060B, BPFP=1.7668 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,660B, BPFP=22.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,672B, BPFP=1.7591 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,652B, BPFP=1.7921 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 57,932B, BPFP=0.8840 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 57,932B, BPFP=0.8840 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 36,124B, BPFP=1.1024 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.49560225 + text_encoder-item0.clip_prompt_embeds 0.00025545 71.88273640 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.43396549 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.07409274 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00124754 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.01535016 1.26440990 + vae.encoder_f1 0.01535382 1.26512825 + vae.decoder 0.00021460 0.02240366 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 4.05338025 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 350504 +BPFP 1.2402 bits/point +EBPFP 2.4804 equivalent bits/point +MSE 4.053380 +---------------------- -------------------------------------------------------- +Time: 0.765s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0534 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,412B, BPFP=25.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,448B, BPFP=1.8193 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,744B, BPFP=23.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,312B, BPFP=1.8922 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 74,672B, BPFP=1.8941 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 50,720B, BPFP=0.7739 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 50,720B, BPFP=0.7739 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 49,256B, BPFP=1.5032 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.47210526 + text_encoder-item0.clip_prompt_embeds 0.00022628 23.78665280 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.55705237 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.08510340 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00132027 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00589589 0.50998384 + vae.encoder_f1 0.00590398 0.50985152 + vae.decoder 0.00017838 0.02726898 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 2.44647231 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 355348 +BPFP 1.2573 bits/point +EBPFP 2.5146 equivalent bits/point +MSE 2.446472 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4465 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,316B, BPFP=24.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,668B, BPFP=1.7137 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,748B, BPFP=23.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,668B, BPFP=1.7588 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 66,864B, BPFP=1.6960 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 68,424B, BPFP=1.0441 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 68,424B, BPFP=1.0441 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 29,120B, BPFP=0.8887 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.468s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.48177838 + text_encoder-item0.clip_prompt_embeds 0.00031548 23.74561181 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.52332687 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.07959194 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00107137 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00725484 1.14497983 + vae.encoder_f1 0.00725992 1.14463294 + vae.decoder 0.00019960 0.01869438 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 2.73855509 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 360296 +BPFP 1.2748 bits/point +EBPFP 2.5496 equivalent bits/point +MSE 2.738555 +---------------------- -------------------------------------------------------- +Time: 0.774s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.468s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7386 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,380B, BPFP=24.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,580B, BPFP=1.5666 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 4,096B, BPFP=25.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,808B, BPFP=1.6078 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 59,500B, BPFP=1.5092 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 59,324B, BPFP=0.9052 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 59,324B, BPFP=0.9052 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,332B, BPFP=0.8036 +⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.48641745 + text_encoder-item0.clip_prompt_embeds 0.00021831 168.11999459 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 0.56489944 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.07500890 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00096339 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00923516 1.09265172 + vae.encoder_f1 0.00923823 1.09296989 + vae.decoder 0.00019521 0.01622261 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 6.49006117 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 329408 +BPFP 1.1655 bits/point +EBPFP 2.3311 equivalent bits/point +MSE 6.490061 +---------------------- -------------------------------------------------------- +Time: 0.776s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.4901 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,300B, BPFP=23.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,560B, BPFP=1.6991 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,656B, BPFP=22.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,372B, BPFP=1.8159 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,636B, BPFP=1.8171 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 61,256B, BPFP=0.9347 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 61,256B, BPFP=0.9347 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 31,744B, BPFP=0.9688 +⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.50395902 + text_encoder-item0.clip_prompt_embeds 0.00062166 191.48464556 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.48646469 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.08166122 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00141707 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00831779 1.06929135 + vae.encoder_f1 0.00832197 1.06910133 + vae.decoder 0.00023271 0.02029689 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 7.09099665 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 353844 +BPFP 1.2520 bits/point +EBPFP 2.5040 equivalent bits/point +MSE 7.090997 +---------------------- -------------------------------------------------------- +Time: 0.785s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.0910 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,368B, BPFP=24.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,860B, BPFP=1.7397 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,652B, BPFP=22.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,236B, BPFP=1.7237 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,820B, BPFP=1.8217 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 52,148B, BPFP=0.7957 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 52,148B, BPFP=0.7957 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 34,216B, BPFP=1.0442 +⌛️ [2/4] FRONTEND: Frontend time: 0.302s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.51537681 + text_encoder-item0.clip_prompt_embeds 0.00022938 167.88309997 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.49972801 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.07634946 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00124272 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00626977 0.69195652 + vae.encoder_f1 0.00627489 0.69191718 + vae.decoder 0.00017842 0.02060118 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 6.29853066 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 337512 +BPFP 1.1942 bits/point +EBPFP 2.3884 equivalent bits/point +MSE 6.298531 +---------------------- -------------------------------------------------------- +Time: 0.778s Load: 0.009s, Pack+Encode: 0.302s, Decode+Unpack: 0.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.2985 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,372B, BPFP=24.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,684B, BPFP=1.7159 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,676B, BPFP=22.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,072B, BPFP=1.7916 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,856B, BPFP=1.7719 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 58,136B, BPFP=0.8871 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 58,132B, BPFP=0.8870 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 46,936B, BPFP=1.4324 +⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.460s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.42722551 + text_encoder-item0.clip_prompt_embeds 0.00022180 48.24660951 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.48497877 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.08620037 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00121572 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00585720 0.56819499 + vae.encoder_f1 0.00586586 0.56821704 + vae.decoder 0.00016520 0.02346971 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 3.11278878 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 360928 +BPFP 1.2771 bits/point +EBPFP 2.5541 equivalent bits/point +MSE 3.112789 +---------------------- -------------------------------------------------------- +Time: 0.764s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.460s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1128 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,380B, BPFP=24.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,560B, BPFP=1.6991 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,556B, BPFP=22.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,112B, BPFP=1.7948 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,784B, BPFP=1.7701 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,260B, BPFP=0.6448 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,256B, BPFP=0.6448 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,684B, BPFP=0.7533 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.52954495 + text_encoder-item0.clip_prompt_embeds 0.00025784 23.68481551 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.45779386 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.07763542 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00125978 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00734802 0.92926228 + vae.encoder_f1 0.00734987 0.92918175 + vae.decoder 0.00018093 0.01495154 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 2.63646998 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 306656 +BPFP 1.0850 bits/point +EBPFP 2.1701 equivalent bits/point +MSE 2.636470 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.6365 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,380B, BPFP=24.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,384B, BPFP=1.8106 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,624B, BPFP=22.6500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,604B, BPFP=1.8347 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,280B, BPFP=1.7319 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 68,024B, BPFP=1.0380 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 68,024B, BPFP=1.0380 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 39,228B, BPFP=1.1971 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.460s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.50658651 + text_encoder-item0.clip_prompt_embeds 0.00023510 84.16739380 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.50067997 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.07716956 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00117310 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00637359 0.87771606 + vae.encoder_f1 0.00637830 0.87792820 + vae.decoder 0.00018566 0.02221701 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 4.19537407 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 372612 +BPFP 1.3184 bits/point +EBPFP 2.6368 equivalent bits/point +MSE 4.195374 +---------------------- -------------------------------------------------------- +Time: 0.762s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.460s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1954 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,404B, BPFP=25.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,088B, BPFP=1.6353 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,732B, BPFP=23.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,420B, BPFP=1.8198 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,188B, BPFP=1.8057 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 50,016B, BPFP=0.7632 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 50,016B, BPFP=0.7632 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,232B, BPFP=0.8005 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.47545513 + text_encoder-item0.clip_prompt_embeds 0.00026418 203.70999053 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.49676242 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.11779821 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00134702 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.01530954 1.25993657 + vae.encoder_f1 0.01531230 1.25808525 + vae.decoder 0.00017892 0.01593295 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 7.49983484 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 325160 +BPFP 1.1505 bits/point +EBPFP 2.3010 equivalent bits/point +MSE 7.499835 +---------------------- -------------------------------------------------------- +Time: 0.757s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.4998 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,420B, BPFP=25.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,868B, BPFP=1.7408 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,660B, BPFP=22.8750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,404B, BPFP=1.8185 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,248B, BPFP=1.8072 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 65,788B, BPFP=1.0038 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 65,784B, BPFP=1.0038 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,412B, BPFP=1.1417 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.51503861 + text_encoder-item0.clip_prompt_embeds 0.00021481 59.82544051 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.47887006 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.08466583 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00127239 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00591154 0.83463204 + vae.encoder_f1 0.00591973 0.83488774 + vae.decoder 0.00025286 0.02320026 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 3.53918718 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 368648 +BPFP 1.3044 bits/point +EBPFP 2.6088 equivalent bits/point +MSE 3.539187 +---------------------- -------------------------------------------------------- +Time: 0.762s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5392 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,332B, BPFP=24.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,520B, BPFP=1.8290 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,652B, BPFP=22.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,908B, BPFP=1.8594 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 74,296B, BPFP=1.8845 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,892B, BPFP=0.6545 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,896B, BPFP=0.6545 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 40,188B, BPFP=1.2264 +⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.50815813 + text_encoder-item0.clip_prompt_embeds 0.00023458 47.94688515 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.52916799 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.09266527 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00134513 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00588703 0.44190615 + vae.encoder_f1 0.00589573 0.44176111 + vae.decoder 0.00053402 0.02477038 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 3.04684522 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 329748 +BPFP 1.1667 bits/point +EBPFP 2.3335 equivalent bits/point +MSE 3.046845 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0468 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,400B, BPFP=25.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,108B, BPFP=1.7733 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,664B, BPFP=22.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,472B, BPFP=1.8240 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,604B, BPFP=1.8163 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 60,792B, BPFP=0.9276 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 60,788B, BPFP=0.9276 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,552B, BPFP=0.8408 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.48815882 + text_encoder-item0.clip_prompt_embeds 0.00022882 107.85202753 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.53379498 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.07884398 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00120391 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00659691 0.98331571 + vae.encoder_f1 0.00660300 0.98428726 + vae.decoder 0.00023739 0.01918747 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 4.86373146 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 349444 +BPFP 1.2364 bits/point +EBPFP 2.4729 equivalent bits/point +MSE 4.863731 +---------------------- -------------------------------------------------------- +Time: 0.765s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8637 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,376B, BPFP=24.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,764B, BPFP=1.7267 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,792B, BPFP=23.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,832B, BPFP=1.7721 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,080B, BPFP=1.7776 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 48,500B, BPFP=0.7401 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 48,496B, BPFP=0.7400 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 47,928B, BPFP=1.4626 +⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.51119224 + text_encoder-item0.clip_prompt_embeds 0.00023928 47.66210515 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.52394419 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.07832755 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00132360 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00583864 0.48258010 + vae.encoder_f1 0.00583800 0.48257831 + vae.decoder 0.00018889 0.02436447 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 3.05761633 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 342832 +BPFP 1.2130 bits/point +EBPFP 2.4261 equivalent bits/point +MSE 3.057616 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0576 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,400B, BPFP=25.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,184B, BPFP=1.7835 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,548B, BPFP=22.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,700B, BPFP=1.8425 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 74,512B, BPFP=1.8900 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,372B, BPFP=0.6160 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,372B, BPFP=0.6160 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 41,332B, BPFP=1.2614 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.51040216 + text_encoder-item0.clip_prompt_embeds 0.00024821 47.70893364 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.43220539 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.08178535 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00155382 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00570467 0.41767281 + vae.encoder_f1 0.00570488 0.41757458 + vae.decoder 0.00017302 0.02057355 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 3.02840795 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 325484 +BPFP 1.1517 bits/point +EBPFP 2.3033 equivalent bits/point +MSE 3.028408 +---------------------- -------------------------------------------------------- +Time: 0.759s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0284 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,336B, BPFP=24.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,004B, BPFP=1.7592 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,612B, BPFP=22.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,220B, BPFP=1.8036 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,656B, BPFP=1.8176 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,176B, BPFP=0.5673 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,172B, BPFP=0.5672 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,456B, BPFP=0.6853 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.53479866 + text_encoder-item0.clip_prompt_embeds 0.00021458 167.97419508 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.45624304 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.08547989 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00123943 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00914783 0.92597222 + vae.encoder_f1 0.00914958 0.92545217 + vae.decoder 0.00017527 0.01502343 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 6.40906363 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 296696 +BPFP 1.0498 bits/point +EBPFP 2.0996 equivalent bits/point +MSE 6.409064 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.4091 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,380B, BPFP=24.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,944B, BPFP=1.8864 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,760B, BPFP=23.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,820B, BPFP=1.9334 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 76,192B, BPFP=1.9326 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 46,640B, BPFP=0.7117 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 46,640B, BPFP=0.7117 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 47,732B, BPFP=1.4567 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.47245594 + text_encoder-item0.clip_prompt_embeds 0.00022150 23.73538116 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.53012567 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.08811031 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00131320 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00578482 0.46843576 + vae.encoder_f1 0.00579739 0.46824351 + vae.decoder 0.00017668 0.02516217 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 2.42571943 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 348172 +BPFP 1.2319 bits/point +EBPFP 2.4639 equivalent bits/point +MSE 2.425719 +---------------------- -------------------------------------------------------- +Time: 0.761s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.4257 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,304B, BPFP=24.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,796B, BPFP=1.7311 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,668B, BPFP=22.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,252B, BPFP=1.7250 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,260B, BPFP=1.7314 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 45,544B, BPFP=0.6949 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 45,544B, BPFP=0.6949 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 36,248B, BPFP=1.1062 +⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.52045540 + text_encoder-item0.clip_prompt_embeds 0.00023894 36.64266268 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.51887636 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.07464081 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00118103 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00958025 0.96991515 + vae.encoder_f1 0.00958229 0.96970177 + vae.decoder 0.00019995 0.02154554 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 2.99485825 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 322680 +BPFP 1.1417 bits/point +EBPFP 2.2835 equivalent bits/point +MSE 2.994858 +---------------------- -------------------------------------------------------- +Time: 0.755s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.9949 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,360B, BPFP=24.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,780B, BPFP=1.8642 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,700B, BPFP=23.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 24,132B, BPFP=1.9588 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 75,452B, BPFP=1.9139 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,692B, BPFP=0.5904 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,692B, BPFP=0.5904 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 48,888B, BPFP=1.4919 +⌛️ [2/4] FRONTEND: Frontend time: 0.322s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.51164599 + text_encoder-item0.clip_prompt_embeds 0.00023387 168.10536729 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.50804906 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.09367111 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00140063 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00567713 0.43403217 + vae.encoder_f1 0.00567905 0.43391028 + vae.decoder 0.00019376 0.02838505 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 6.18639084 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 332760 +BPFP 1.1774 bits/point +EBPFP 2.3548 equivalent bits/point +MSE 6.186391 +---------------------- -------------------------------------------------------- +Time: 0.795s Load: 0.008s, Pack+Encode: 0.322s, Decode+Unpack: 0.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.1864 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,404B, BPFP=25.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,228B, BPFP=1.7895 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,724B, BPFP=23.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,440B, BPFP=1.8214 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 74,280B, BPFP=1.8841 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 46,004B, BPFP=0.7020 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 45,996B, BPFP=0.7018 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 31,420B, BPFP=0.9589 +⌛️ [2/4] FRONTEND: Frontend time: 0.306s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.47169113 + text_encoder-item0.clip_prompt_embeds 0.00024281 36.12568909 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.46726584 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.10866561 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00131522 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.02387581 1.12059629 + vae.encoder_f1 0.02387858 1.11977577 + vae.decoder 0.00018648 0.01889406 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 3.05222590 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 326560 +BPFP 1.1555 bits/point +EBPFP 2.3109 equivalent bits/point +MSE 3.052226 +---------------------- -------------------------------------------------------- +Time: 0.770s Load: 0.008s, Pack+Encode: 0.306s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0522 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,372B, BPFP=24.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,584B, BPFP=1.7024 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,552B, BPFP=22.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,060B, BPFP=1.7906 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 69,864B, BPFP=1.7721 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 68,584B, BPFP=1.0465 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 68,580B, BPFP=1.0464 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,640B, BPFP=0.7520 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.461s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.48981909 + text_encoder-item0.clip_prompt_embeds 0.00022399 47.85840267 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.48056188 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.08213598 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00119573 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.01169517 1.40762591 + vae.encoder_f1 0.01169969 1.40783429 + vae.decoder 0.00021186 0.01627092 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 3.49098394 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 359300 +BPFP 1.2713 bits/point +EBPFP 2.5426 equivalent bits/point +MSE 3.490984 +---------------------- -------------------------------------------------------- +Time: 0.763s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.461s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4910 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,384B, BPFP=24.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,808B, BPFP=1.7327 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,656B, BPFP=22.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,944B, BPFP=1.7812 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 75,712B, BPFP=1.9205 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 54,376B, BPFP=0.8297 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 54,372B, BPFP=0.8297 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 38,240B, BPFP=1.1670 +⌛️ [2/4] FRONTEND: Frontend time: 0.302s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.45594700 + text_encoder-item0.clip_prompt_embeds 0.00022123 155.84077381 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.47971334 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.07928349 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00140991 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.32749966 3.91943169 + vae.encoder_f1 0.32750070 3.92059422 + vae.decoder 0.00039956 0.02569227 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 7.48135368 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 350556 +BPFP 1.2404 bits/point +EBPFP 2.4807 equivalent bits/point +MSE 7.481354 +---------------------- -------------------------------------------------------- +Time: 0.773s Load: 0.008s, Pack+Encode: 0.302s, Decode+Unpack: 0.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.4814 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,364B, BPFP=24.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,844B, BPFP=1.7376 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,664B, BPFP=22.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 22,448B, BPFP=1.8221 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,832B, BPFP=1.7206 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 47,140B, BPFP=0.7193 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 47,136B, BPFP=0.7192 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 38,412B, BPFP=1.1722 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.46559858 + text_encoder-item0.clip_prompt_embeds 0.00024675 35.78797179 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.46580000 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.08201320 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00120209 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00566967 0.50423276 + vae.encoder_f1 0.00567867 0.50439566 + vae.decoder 0.00017839 0.02193683 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 2.75694351 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 328904 +BPFP 1.1638 bits/point +EBPFP 2.3275 equivalent bits/point +MSE 2.756944 +---------------------- -------------------------------------------------------- +Time: 0.765s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7569 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,408B, BPFP=25.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,452B, BPFP=1.5492 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,828B, BPFP=23.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 20,456B, BPFP=1.6604 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 70,032B, BPFP=1.7764 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 45,660B, BPFP=0.6967 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 45,660B, BPFP=0.6967 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 50,948B, BPFP=1.5548 +⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.48592083 + text_encoder-item0.clip_prompt_embeds 0.00022364 107.92449608 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.54190516 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.07518586 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00119073 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00580750 0.47623336 + vae.encoder_f1 0.00580664 0.47622082 + vae.decoder 0.00018044 0.02692396 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 4.63096956 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 337508 +BPFP 1.1942 bits/point +EBPFP 2.3884 equivalent bits/point +MSE 4.630970 +---------------------- -------------------------------------------------------- +Time: 0.771s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.6310 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,380B, BPFP=24.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,632B, BPFP=1.7089 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,588B, BPFP=22.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,372B, BPFP=1.7347 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 68,360B, BPFP=1.7340 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 50,472B, BPFP=0.7701 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 50,472B, BPFP=0.7701 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 31,064B, BPFP=0.9480 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.49800670 + text_encoder-item0.clip_prompt_embeds 0.00030118 179.62229437 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.46345181 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.07617334 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00126489 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.03869025 1.40097237 + vae.encoder_f1 0.03869358 1.39951265 + vae.decoder 0.00021614 0.01955744 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 6.93390556 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 327404 +BPFP 1.1584 bits/point +EBPFP 2.3169 equivalent bits/point +MSE 6.933906 +---------------------- -------------------------------------------------------- +Time: 0.762s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.9339 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,292B, BPFP=23.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 12,120B, BPFP=1.6396 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,808B, BPFP=23.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,352B, BPFP=1.7331 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,904B, BPFP=1.7224 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 68,412B, BPFP=1.0439 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 68,408B, BPFP=1.0438 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,020B, BPFP=0.9161 +⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.53400755 + text_encoder-item0.clip_prompt_embeds 0.00023260 156.50924986 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.52001972 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.08282731 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00123678 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00839879 1.36940098 + vae.encoder_f1 0.00840224 1.37028241 + vae.decoder 0.00019463 0.01890228 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 6.31554190 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 361380 +BPFP 1.2787 bits/point +EBPFP 2.5573 equivalent bits/point +MSE 6.315542 +---------------------- -------------------------------------------------------- +Time: 0.763s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3155 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,288B, BPFP=23.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 14,128B, BPFP=1.9113 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,760B, BPFP=23.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 23,604B, BPFP=1.9159 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,176B, BPFP=1.8054 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 62,484B, BPFP=0.9534 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 62,484B, BPFP=0.9534 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,760B, BPFP=0.8472 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.50109458 + text_encoder-item0.clip_prompt_embeds 0.00023544 24.07707234 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.54571743 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.09217347 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00152231 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.01160815 1.23222768 + vae.encoder_f1 0.01161249 1.23235321 + vae.decoder 0.00021720 0.01868590 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 2.78842633 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 354748 +BPFP 1.2552 bits/point +EBPFP 2.5104 equivalent bits/point +MSE 2.788426 +---------------------- -------------------------------------------------------- +Time: 0.761s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 2.7884 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,376B, BPFP=24.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,100B, BPFP=1.7722 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,656B, BPFP=22.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,516B, BPFP=1.7464 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,020B, BPFP=1.8014 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 41,604B, BPFP=0.6348 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 41,604B, BPFP=0.6348 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,120B, BPFP=0.7361 +⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.455s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.45109256 + text_encoder-item0.clip_prompt_embeds 0.00022923 84.55468750 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.49090524 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.07911858 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00120127 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.02989292 1.27518702 + vae.encoder_f1 0.02989391 1.27458394 + vae.decoder 0.00034944 0.01871053 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 4.38930697 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 306060 +BPFP 1.0829 bits/point +EBPFP 2.1658 equivalent bits/point +MSE 4.389307 +---------------------- -------------------------------------------------------- +Time: 0.756s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.455s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.3893 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 2,404B, BPFP=25.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 13,344B, BPFP=1.8052 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 3,844B, BPFP=24.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 21,808B, BPFP=1.7701 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 71,352B, BPFP=1.8099 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 2,600B, BPFP=27.0833 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 10,312B, BPFP=1.3950 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 4,528B, BPFP=28.3000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 19,440B, BPFP=1.5779 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 50,184B, BPFP=1.2729 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 64,476B, BPFP=0.9838 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 64,476B, BPFP=0.9838 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 37,356B, BPFP=1.1400 +⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 0.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.47943628 + text_encoder-item0.clip_prompt_embeds 0.00024627 155.74419981 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.52412682 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.07945030 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00145136 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 + text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 + vae.encoder_f0 0.00613025 0.64620739 + vae.encoder_f1 0.00613536 0.64596856 + vae.decoder 0.00018697 0.02118693 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 5.96000960 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 366124 +BPFP 1.2954 bits/point +EBPFP 2.5909 equivalent bits/point +MSE 5.960010 +---------------------- -------------------------------------------------------- +Time: 0.769s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.9600 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.2040 bits/point +Avg EBPFP 2.4079 equivalent bits/point +Avg MSE 4.305220 +Avg Time 0.772s +------------------------ ----------------------------